VOCATIONAL INTEREST AND OTHER NON-COGNITIVE FACTORS …

VOCATIONAL INTEREST AND OTHER NON-COGNITIVE

FACTORS AS PREDICTORS OF ACADEMIC

PERFORMANCE IN HIGH SCHOOL

by

Elton Jeremy Bloye

A minor-dissertation submitted in partial fulfilment of the

requirements for the degree of

Master in Science in Psychology

at the

University of Johannesburg

2007

Supervisor: Dr K de Bruin

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ACKNOWLEDGEMENTS

• I would firstly like to acknowledge my Creator, Saviour and Friend, Jesus Christ. To Him be

all the honour and glory.

• Thank you to the Grade 10 pupils as well as Mr. Anton Dempsey, Mrs. Linda Blore, the

Department of Life Orientation and the Grade 10 Grade Tutors at Randpark High School.

This study would not have been possible without your input and effort.

• A heartfelt thank you to Dr. Karina De Bruin, a supervisor better “then” the rest. Thank you

for your experience, solid advice, patience and encouragement throughout the process.

• Prof. Gideon P. De Bruin (University of Johannesburg) and Prof. Herman Aguinis

(University of Colorado) for their technical input and great ideas that made the study

workable.

• Rene Van Eeden (University of South Africa) for her initial ideas about the research topic.

• The National Research Foundation (NRF) for granting a bursary that was used to fund the

project.

• The University of Johannesburg for awarding a student bursary that assisted with funding the

degree.

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• The staff of Statistical Consultation Services (STATCON) that assisted with the data

capturing and analysis.

• My wife Ilse and Morgan for their constant love, support, patience and encouragement.

• Pastor Wayne Gordon for his consistent encouragement and support and Mr. Craig Aitchison

for his help with the data analysis during the initial stages of the project.

• The Psychology Masters group of 2006 as well the Psychology Department at the University

of Johannesburg for consistently supporting me in the study.

The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF.

INDEX

Summary

Opsomming

CHAPTER 1 OVERVIEW OF STUDY

1.1 INTRODUCTION 14

1.2 PROBLEM STATEMENT 15

1.3 PURPOSE OF THE STUDY 17

1.4 DEFINITIONS OF CONSTRUCTS 18

1.4.1 Academic ability 18

1.4.2 Cognitive factors 18

1.4.3 Non cognitive factors 18

1.4.4 Vocational interest 18

1.4.5 Self-efficacy 19

1.4.6 Person-environment fit 19

1.4.7 Achievement motivation 19

1.4.8 Coping strategies 19

1.4.9 Self-directedness in learning 19

1.4.10 Avoidance of procrastination 20

1.4.11 Academic performance 20

1.4.12 Social Cognitive Career theory 20

1.5 OVERVIEW OF THE STUDY 20

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CHAPTER 2 LITERATURE REVIEW

2.1 INTRODUCTION 22

2.2 COGNITIVE PREDICTORS OF ACADEMIC PERFORMANCE 24

2.2.1 Academic ability and intelligence 24

2.2.1.1 The General Scholastic Aptitude Test as a measure of academic ability 26

2.2.1.2 Academic ability as a predictor of academic performance 27

2.3 NON-COGNITIVE FACTORS AFFECTING ACADEMIC PERFORMANCE 28


2.3.1.1 Vocational interest, personality and cognitive ability 29

2.3.1.2 Holland’s theory of vocational personalities and work environments 31

2.3.1.3 Measurement of vocational interest 33

2.3.1.4 Gender and culture differences with respect to vocational interest 34

2.3.1.5 Vocational interest as a predictor of academic performance 36

2.3.1.6 Influence of vocational interest on educational and occupational pathways 36

2.3.2 Person-environment fit 38

2.3.2.1 Holland’s congruence theory 38

2.3.2.2 Person-environment fit and academic performance 39

2.3.3 Self-efficacy 40

2.3.4 Achievement motivation 44

2.3.5 Coping strategies 46

2.3.6 Self-directedness in learning 48

2.3.7 Avoidance of procrastination 49

2.3.8 Conclusion 51

2.4 CONTEXTUAL FACTORS AFFECTING ACADEMIC PERFORMANCE 51

2.4.1 The influence of significant others 52

2.4.2 The influence of socio-economic factors 52

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2.5 SOCIAL-COGNITIVE CAREER THEORY EXPLAINING DIFFERENCES IN

ACADEMIC PERFORMANCE 53

2.6 CHAPTER SUMMARY 54

CHAPTER 3 RESEARCH METHOD

3.1 INTRODUCTION 56

3.2 RESEARCH PROBLEM 56

3.3 PURPOSE OF THE STUDY 58

3.4 PARTICIPANTS 59

3.5 MEASUREMENT INSTRUMENTS 59

3.5.1 General Scholastic Aptitude Test (GSAT) 60

3.5.1.1 Uses of the GSAT 61

3.5.1.2 Description of the subtests 61

3.5.1.3 Reliability and validity of the GSAT 62

3.5.2 Self-Directed Search (SDS) 63

3.5.2.1 Purpose of the SDS 63

3.5.2.2 Uses of the SDS 64


3.5.2.4 Reliability and validity of the SDS 65

3.5.3 Academic Behaviours and Attitudes Questionnaire (ABAQ) 67

3.5.3.1 Purpose of the ABAQ 67

3.5.3.2 Uses of the ABAQ 68


3.5.3.4 Reliability and validity of the ABAQ 68

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3.6 PROCEDURE 70

3.7 RESEARCH HYPOTHESES 72

3.7.1 Research hypothesis 1 72



3.8 STATISTICAL ANALYSIS 73

3.8.1 Descriptive statistics 74

3.8.2 Inferential statistics pertaining to Hypothesis 1 74

3.8.2 Inferential statistics pertaining to Hypotheses 2 and 3 75


CHAPTER 4 RESULTS

4.1 INTRODUCTION 77

4.2 DESCRIPTIVE STATISTICS 77

4.3 RESULTS PERTAINING TO HYPOTHESIS 1 79


4.4.1 Results pertaining to Accounting 82

4.4.2 Results pertaining to Business Economics 83

4.4.3 Results pertaining to English 85

4.4.4 Results pertaining to Life Orientation 88

4.4.5 Results pertaining to Life Science 90

4.4.6 Results pertaining to Mathematics 91


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CHAPTER 5 DISCUSSION OF FINDINGS

5.1 INTRODUCTION 97

5.2 VARIABLES AFFECTING ACADEMIC PERFORMANCE 98

5.2.1 Academic ability 98


5.2.3 Academic attitudes and study behaviours 104

5.2.3.1 Self-efficacy and academic performance 105

5.2.3.2 Person-environment fit and academic performance 106

5.2.3.3 Achievement motivation and academic performance 106

5.2.3.4 Self-directedness learning, Coping and academic performance 107

5.2.3.5 Avoidance of procrastination and academic performance 108

5.3 A NEW EXPLANATORY MODEL FOR HIGH SCHOOL STUDENTS 109

5.4 IMPLICATIONS OF THE RESEARCH FINDINGS 113

5.5 LIMITATIONS OF THE STUDY AND IMPLICATIONS FOR FUTURE

RESEARCH 114

5.6 CONCLUSION 117

REFERENCES 118

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INDEX OF FIGURES

Figure 2.1 Spearman’s two factor theory of abilities 25

Figure 2.2 Vernon’s hierarchical model of the organisation of the ability factors 26

Figure 2.3 Hexagonal model illustrating relative distances among personality types 32

Figure 2.4 Model of person, contextual and experiential factors affecting career related

choice behaviour 37

Figure 2.5 Social Cognitive Interest Model 42

Figure 2.6 Social Cognitive Performance Model 43

Figure 2.7 Model of Task Performance 54

Figure 5.1 Social Cognitive Interest Model 109

Figure 5.2 Social Cognitive Performance Model 110


choice behaviour 111

Figure 5.4 Explanatory model for academic performance in high school students 112

INDEX OF TABLES

Table 3.1 Correlation coefficients for shortened GSAT and examination marks 63

Table 3.2 Reliability coefficients for SDS adaptation study (Sichel formula) 66

Table 3.3 Intercorrelations of the SDS fields (1987 study) 66

Table 3.4 SDS reliability statistics for a sample of high school students 67

Table 3.5 Reliability coefficients for the ABAQ across language and gender groups 69

Table 4.1 Age statistics for sample of 285 Grade 10 students 77

Table 4.2 Gender statistics for sample of 285 Grade 10 students 78

Table 4.3 Racial designation statistics for sample of 285 Grade 10 students 78

Table 4.4 Home language statistics for sample of 285 Grade 10 students 79

Table 4.5 Predictive effect of academic ability on overall academic performance 80

Table 4.6 Subjects considered in study with corresponding vocational interests 81

Table 4.7 Predictive effect of vocational interests on academic performance in

Accounting 82

Table 4.8 Regression weights, t-tests and effect sizes in the prediction of academic

performance in Accounting 83

Table 4.9 Predictive effects of vocational interests on academic performance in Business

Economics 84


performance in Business Economics 85

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Table 4.11 Predictive effects of vocational interests on academic performance in

English 86


performance in English 87

Table 4.13 Predictive effects of vocational interests on academic performance in Life

Orientation 88


performance in Life Orientation 89


Sciences 90


performance in Life Sciences 91


Mathematics 92


performance in Mathematics 93

Table 4.19 Predictive effects of ABAQ factors on overall academic performance 94

Table 4.20 Regression weights, t-tests and effect sizes pertaining to the predictive effects of

ABAQ factors on overall academic performance 95

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SUMMARY

Research has indicated that there are many factors affecting academic performance of high school

students, which in turn can have a significant effect on their future educational and occupational

opportunities. While much international research has been done on cognitive and non-cognitive

factors affecting academic performance, there seems to be a lack of empirical studies within the

South African context, especially with regard to the effect of vocational interests, academic

attitudes and study behaviours. The study investigated three hypotheses. Firstly, academic ability

has a significant influence on school students’ academic performance; secondly, school students

who show vocational interest patterns that correspond with specific subject content, perform

academically better than school students who do not have interests that are in line with the subject

content; and thirdly, school students with positive academic attitudes and study behaviours perform

academically better than students with negative academic attitudes and study behaviours. The study

included 285 Grade 10 students who completed the General Scholastic Aptitude Test, the Self-

Directed Search and the Academic Behaviours and Attitudes Questionnaire. The results of multiple

regression analyses revealed that academic ability, vocational interests, self-efficacy, achievement

motivation, self-directedness in learning and avoidance of procrastination all contributed toward

predicting academic performance. With regard to the role of vocational interests, the results also

revealed that Investigative and Realistic interests had a significant effect on academic performance

even when subject content did not match vocational interest patterns. An adjusted model, based on

Social Cognitive Career Theory was formulated in order to conceptualise the study.

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OPSOMMING

Navorsing het aangetoon dat verskeie faktore die akademiese prestasie van hoërskoolleerlinge

beïnvloed. Akademiese prestasie speel op sy beurt ‘n betekenisvolle rol in die toekomstige

opvoedkundige en loopbaangeleenthede van leerlinge. Op internasionale vlak is baie navorsing

gedoen rakende kognitiewe en nie-kognitiewe faktore wat akademiese prestasie beïnvloed. Dit blyk

egter asof daar ‘n gebrek aan empiriese studies binne die Suid-Afrikaanse konteks is, veral met

verwysing na die invloed van beroepsbelangstellings, akademiese houdings en studiegedrag.

Hierdie studie het drie navorsingshipoteses ondersoek. Eerstens, akademiese vermoë het ‘n

betekenisvolle invloed op leerlinge se akademiese prestasie; tweedens, leerlinge wat

beroepsbelangstellingspatrone toon wat ooreenstem met spesifieke vakinhoude, vaar akademies

beter as leerlinge wie se belangstellings nie ooreenstem met die vakinhoude nie; en derdens,

leerlinge met positiewe akademiese houdings en studiegedrag vaar akademies beter as leerlinge met

negatiewe akademiese houdings en studiegedrag. Tweehonderd-vyf-en-tagtig Graad 10 leerlinge

het die Algemene Skolastiese Aanlegtoets, die Selfondersoekvraelys en die Akademiese Gedrag en

Houdingsvraelys voltooi. Die resultate van meervoudige regressie-ontledings het aangedui dat

akademiese vermoë, beroepsbelangstelling, self-effektiwiteit, prestasiemotivering, self-

rigtinggewendheid ten opsigte van leer en vermyding van uitstel betekenisvolle bydraes tot die

voorspelling van akademiese prestasie gelewer het. Met verwysing na die rol van

beroepsbelangstellings het die resultate ook aangedui dat Ondersoekende en Realistiese

belangstellings ‘n betekenisvolle bydrae gelewer het tot akademiese prestasie, selfs in gevalle waar

die vakinhoud nie met die beroepsbelangstelling ooreengestem het nie. ‘n Gewysigde model,

gebaseer op Sosiaal-Kognitiewe Loopbaanteorie, is geformuleer om die studie te konseptualiseer.

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CHAPTER ONE

OVERVIEW

1.1 INTRODUCTION

When considering the world of education and work, academic performance amongst school

students is a significant factor that affects the opportunity for entry into various educational and

vocational settings. High academic performers find it easier to gain entry into further education and

employment opportunities while low academic performers find it more difficult. In this light,

academic performance may be regarded as an important factor that shapes a particular educational

and vocational pathway for an individual at the expense of other educational and vocational

pathways.

There are many factors which may influence academic performance at a physiological,

psychological, sociological and metaphysical level. Much research has been done on the role of

cognitive factors, namely intelligence and academic ability, in predicting academic performance at

a school level. This literature proposes that academic ability is the predominant factor in

determining academic success in adolescents (Furnham & Chamorro-Premuzic, 2004; Grobler,

Grobler & Esterhuyse, 2001; Lau & Roser, 2002; Masqud, 1983; Midkiff, Burke & Helmstadter,

1989). Despite the fact that research generally shows a significant positive correlation between

academic ability and academic performance, there has been a recent shift in emphasis from studying

cognitive predictors to examining the role of non-cognitive factors such as personality constructs

(Lounsbury, Sundstrom, Loveland & Gibson, 2003). Many non-cognitive factors such as interests,

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academic attitudes and study behaviours also seem to play a role in predicting academic

performance. For example, Lent, Lopez and Bieschke (1991) found that self-efficacy had a

significant influence on Mathematics grades. Significant positive correlations between expectations,

motivation, self-confidence and academic performance were also reported (Tavani & Losh, 2003).

In a recent study, Sparfeldt (2007) showed that gifted individuals with high academic performance

displayed more interest in scientific/investigative activities than average achievers. Research into

non-cognitive factors has also fostered the development of new theories of task performance such

as the Social Cognitive Career Theory proposed by Lent, Hackett and Brown (1996). They propose

that self-efficacy has a positive relationship with vocational interests and this in turn affects

academic performance. Despite the relative influence of cognitive and non-cognitive factors on

academic performance, how these factors relate to one another, remains unclear (Furnham &

Chammorro-Premuzic, 2004). They should however be the object of further research as the various

factors affecting academic performance at a school level may directly and indirectly determine the

level of educational and occupational opportunity available to a school student.

1.2 PROBLEM STATEMENT

School students often believe that cognitive factors or “intelligence” is the predominant influence in

determining academic success at school. A lack of knowledge or a general disregard amongst

students on the importance of the various non-cognitive factors influencing academic performance

may influence choices, attitudes and behaviours that hinder optimal academic performance rather

than promoting it. Vocational interest has been reported as one of the important non-cognitive

factors that may influence academic performance (Brown, 1994; Holland, 1968; Schneider &

Overton, 1983). School students who choose subjects for which they hold little interest or for which

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they fail to see the connection between subject content and its relevance toward their educational

and vocational goals, may perform less well academically than school students who see this

connection and are interested in the subjects they take. Holland (1997) states that level of

accomplishment in a particular field is positively associated with the level of congruency between

interest type and the field’s environment. Other important non-cognitive factors affecting academic

performance include academic attitudes and study behaviours. Attitudes such as self-efficacy

(Siegel, Galassi & Ware, 1985) and achievement motivation (Tavani & Losh, 2003) have been

found to have a strong positive association with performance at school. Study behaviours such as

the use of coping strategies (Zuckerman, Kieffer & Knee, 1998), self-directedness in learning (Long

& Morris, 1996) and avoidance of procrastination (Chu & Choi, 2005) also have been reported to

influence academic performance. Person-environment fit is an additional factor that has been linked

to academic success at school (Feldman, Smart & Ethington, 1999).

A lack of knowledge or general disregard of the importance of factors such as vocational interests,

academic attitudes and study behaviours amongst school students may be related to lower academic

performance. Social Cognitive Career Theory (Lent et al., 1996) suggests that workplace

performance is positively associated with vocational interests, therefore one may hypothesise that a

disregard for this factor at a school level may also lead to poor academic performance, decreasing

the opportunity for entry into further education and training as well as limiting valuable career

options. Also, if school students realise on the basis of objective research that maintaining positive

academic attitudes and study behaviours will increase their academic performance, they may adopt

these attitudes and behaviours. With this in mind, it seems important that research be conducted to

establish the influence of these non-cognitive factors on academic performance so that measures

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can be put into place to make school students and their parents aware of the significant impact

thereof on academic results. While the influence of certain non-cognitive factors such as self-

efficacy and personality factors have been well researched (cf. Ackerman & Heggestad, 1997;

Andrew, 1998; Barrick, Mount & Gupta, 2003; Bong, 2002; De Fruyt & Mervielde, 1999; Mount,

Barrick, Scullen & Rounds, 2005; Sullivan & Hansen, 2004), the relationship between vocational

interest and academic performance is in need of more research, especially within the South African

context. In addition, most research on the relationship between academic attitudes and study

behaviours has been conducted in university settings and not at a high school level. Therefore, it

seems appropriate that more empirical data be gathered in high school settings within a South

African context.

1.3 PURPOSE OF THE STUDY

The purpose of this study is to investigate the relationship between academic performance and non-

cognitive factors. These factors include vocational interest, person-environment fit, academic

attitudes such as self-efficacy and achievement motivation, and study behaviours such as the use of

coping skills, self-directedness in learning and avoidance of procrastination. The results of the study

may lead to valuable information about factors, other than cognitive factors, that influence

academic performance. In addition to this, the research may assist in developing and implementing

interventions in both a home and school setting that will assist school students in improving their

academic performance.

Research which shows a relationship between vocational interest and academic performance may

encourage learners to choose subjects on the basis of what they are interested in with regard to their

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future career, thereby increasing their chances of better academic performance. The purpose of this

study therefore is to provide empirical data regarding the relationship between vocational interests,

academic attitudes, study behaviours and academic performance.

1.4 DEFINITIONS OF CONSTRUCTS

1.4.1 Academic ability

Academic ability is defined according to Vernon’s (1950) definition of the verbal educational

aptitude (v.ed.) cited in Jensen (1980). Vernon describes v.ed. as a characteristic of numerical,

verbal and logical reasoning. Claassen, De Beer, Hugo and Meyer (1998) state that the purpose of

the General Scholastic Aptitude Test (GSAT), which is used to operationalise academic ability in

this study, is largely to determine the verbal educational factor.

1.4.2 Cognitive factors

In this study, cognitive factors relate to the definition of academic ability in paragraph 1.4.1.

1.4.3 Non-cognitive factors

For purposes of this study, non-cognitive factors refer to vocational interest, self-efficacy, person-

environment fit, achievement motivation, coping strategies, self-directedness in learning and

avoidance of procrastination.

1.4.4 Vocational interest

Vocational interest, as defined by Holland (1997) is an expression of an individual’s personality in

work, school subjects, hobbies, recreational activities and preferences.

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1.4.5 Self-efficacy

Self-efficacy is defined as a person’s beliefs about his or her ability or confidence to bring about

intended results (Colman, 2006).

1.4.6 Person-environment fit

Person-environment fit is defined as the degree to which the personality and the environment match

(Nel, 2006).

1.4.7 Achievement motivation

Achievement motivation is described as the striving tendency towards success with the associated

positive effects and towards the avoidance of failure and the associated negative effects (Busato,

Prins, Elshout & Hamaker, 2000).

1.4.8 Coping strategies

Coping strategies are defined as the cognitive and behavioural tactics that individuals utilise to

control their environmental surroundings and to alleviate any stress which may occur when

environmental demands surpass individuals’ resources (Collins & Onwuegbuzie, 2003).

1.4.9 Self-directedness in learning

Self-directedness in learning is defined according to Knowles (1975) as the process in which

individuals take the initiative to identify their learning needs, formulate learning goals, identify

resources for learning, choose and implement learning strategies and evaluate learning outcomes.

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1.4.10 Avoidance of procrastination

Procrastination is defined as the lack or absence of self-regulated performance and the behavioural

tendency to postpone what is necessary to reach a goal (Ellis & Knaus, 1977). An avoidance of

procrastination can be defined as an avoidance of this kind of behaviour.

1.4.11 Academic performance

Academic performance is defined in line with Kobal and Musek’s (2001) definition, which refers to

the numerical scores of a student’s knowledge, representing the degree of a student’s adaptation to

schoolwork and the educational system.

1.4.12 Social Cognitive Career theory

Social Cognitive Career Theory, according to Lent, Brown and Hackett (2002) attempts to

consolidate constructs such as self-concept, self-efficacy, interests and abilities and composes a

comprehensive explanatory system of the complex connections between persons and their career-

related contexts

1.5 OVERVIEW OF THE STUDY

In Chapter Two, the relevant literature pertaining to the study of cognitive and non-cognitive factors

affecting academic performance is reviewed. Theoretical orientations and empirical data relating to

academic ability, vocational interest, person-environment fit, self-efficacy, achievement motivation,

coping strategies, self-directedness in learning and avoidance of procrastination are discussed. This

is followed by a review of Social Cognitive Career Theory as an explanatory model. Chapter Three

provides an overview of the research problem as well as the main aims and purposes of the study.

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The participants, research instruments, research hypotheses, research procedures and statistical

methods used to analyse the data, are also described in this chapter.

Chapter Four provides a summary of the results of the study. More specifically, it presents a

summary of the descriptive data, including factors such as gender, age, racial designation and

language groups. The chapter also presents a summary of the simple and multiple regression

analyses pertaining to the hypotheses. Chapter Five presents a discussion of the results. The results

of the study are reviewed followed by a discussion of the implications of the results in light of the

literature review and implications for future research.

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CHAPTER TWO

LITERATURE REVIEW

2.1 INTRODUCTION

Chapter Two reviews the current literature pertaining to the study of cognitive and non-cognitive

academic factors affecting academic performance. Definitions of the constructs are followed by a

review of the theoretical orientations and empirical data relating to the influence of academic

ability, vocational interest, person-environment fit, self-efficacy, achievement motivation, coping,

self-directedness in learning, avoidance of procrastination and contextual factors on academic

achievement. The chapter concludes with an overview of Social-Cognitive Career Theory as an

explanatory model for academic achievement.

According to Paa and McWhirter (2000), the adolescent years are important in laying a foundation

for future career and educational pursuits. An individual’s academic performance and subject

choices can affect entry into further education and training opportunities which may shape

particular educational and vocational pathways for an individual at the expense of other educational

and vocational pathways. This factor is especially relevant in the South African context in which a

large proportion of the population comes from disadvantaged backgrounds. Botha, Brand, Cilliers,

Davidow, de Jager and Smith (2005) highlight that students who are academically unprepared and

enter into higher education settings are more likely to experience adjustment problems to university

life. It is therefore not surprising that researchers have conducted empirical studies to investigate

factors affecting academic performance. For example, Aitken (1994) conducted research among

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6500 first degree graduates in the United Kingdom who had just entered the labour market. He

concludes that success in the career marketplace depended on past academic performance, as well

as subject-choices and socio-economic factors. In a similar study, Athanasou (2001) investigated

factors affecting Australian school leavers’ educational-vocational achievement and found that the

most powerful influences on ultimate educational-vocational achievement were academic

achievement in literacy and numeracy, the completion of Grade 12 (final year of schooling) and

vocational interests.

With regard to subject choice and academic performance, Ainley, Jones and Navratnam (1990) state

that subjects studied in senior secondary years are a major influence on the educational and career

options available to young people upon leaving school. Research into the subject selection process

shows that academic performance in a particular subject is an influential factor affecting the choice

of that subject for senior schooling. Early work by Ball (1981) and Woods (1976, 1979), as

described in Stables (1997), concludes that selection of certain subjects is constrained by factors

operating within and beyond the school, one of these factors being academic ability. Research with

school students in the United Kingdom conducted by Garrat (1985) and Kelly (1988) shows that

previous performance of school students in O-Level subjects was a significant factor in determining

the choice and continuation of certain A-level subjects until the end of formal schooling. In

Australia, Ainley et al. (1990) and Dellar (1994) report strong relationships between subject

enrolment and school students’ level of achievement. Dellar (1994) also found that lower ability

school students tended to select subjects in which they had gained prior success and eliminated

subjects which they perceived as difficult.

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It is important to note the difference between academic ability and academic performance. While

academic ability refers to a cognitive phenomenon that forms part of the concept of intelligence, or

the ability to succeed in academically related activities, academic performance is a measure of

success in the academic task undertaken. Jensen (1980) states that many formal definitions suggest

some kind of distinction between intelligence and performance and it is recognised that a person of

high academic ability may be affected by various non-cognitive and contextual factors that may

increase or decrease his or her academic performance. A review of some these non-cognitive and

contextual factors appears later in the present chapter, however it is first appropriate to review the

cognitive factors affecting academic performance.

2.2 COGNITIVE PREDICTORS OF ACADEMIC PERFORMANCE

2.2.1 ACADEMIC ABILITY AND INTELLIGENCE

The concept of academic ability is historically derived from the construct of intelligence. There are

many different definitions of intelligence, some confined to more cognitive constructs while others

taking into account non-cognitive factors. The concept of academic ability involves the cognitive or

intellectual part of intelligence rather than the non-cognitive constructs. According to Kail and

Pellegrino (1985), most people understand intelligence to imply at least two specific concepts,

namely exceptional linguistic ability, evident in having a large vocabulary and good reading

comprehension, and problem-solving ability which is evident in having good logical reasoning,

applying knowledge to problems and making sound decisions. Cited in Jensen (1980), David

Wechsler, developer of the Wechsler Adult Intelligence Scale (WAIS), sees intelligence as

involving personality and values as well as cognition. Mwamwenda (1995) takes into account

cultural phenomena and defines intelligence as what enables a person to think, act and behave in a

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manner that is normally acceptable to their society, thus facilitating his or her adjustment socially,

intellectually and physically.

The concept of academic ability was borne out of intelligence testing which began with Sir Francis

Galton (1822-1911) on the premise that people differ with regard to their sensory, perceptual and

motor processes (Jordaan & Jordaan, 1998). It was however Alfred Binet who developed

intelligence testing in the context of cognitive academic ability. Binet developed a test to

distinguish children who were ready for formal schooling from those who needed a remedial

programme (Mwamwenda, 1995). Since then there have been many different theories as to what

constitutes cognitive ability as an aspect of intelligence. The most notable pioneer in this area was

Charles Spearman who hypothesised a two-factor theory of intelligence. Spearman’s (1927) theory

held that a test of cognitive ability measures a general factor (g), and a specific factor (s) which was

unique to that particular test (Jensen, 1980). His theory is depicted in Figure 2.1.

Figure 2.1 Spearman’s two factor theory of abilities

Source: Copyright © 1980 by A.R. Jensen. Reprinted with permission from Methuen, London.

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Jensen (1980) states that the most reasonable overall picture from factor analytical studies

of mental abilities is provided by Vernon (1950). Vernon acknowledges Spearman’s g factor, but

applies two major group factors stemming from the g factor, namely a factor of verbal educational

aptitude (v:ed) and spatial mechanical aptitude (k:m). V:ed is characteristic of numerical, verbal and

logical reasoning tests while k:m is characteristic of tests involving spatial visualisation and an

understanding of physical and mechanical principles. Stemming from the major group factors are

minor group factors or primary abilities. Finally, from the minor group factors stem small factors

specific to each test. This model is shown diagrammatically in Figure 2.2.

Figure 2.2 Vernon’s hierarchical model of the organisation of the ability factors

Source: Copyright © 1980 by A.R. Jensen. Reprinted with permission from Methuen, London.

2.2.1.1 The General Scholastic Aptitude Test (GSAT) as a measure of academic ability

Although many tests of cognitive ability may be relevant in the South African context, the General

Scholastic Aptitude Test (GSAT) is a group test designed to measure academic intelligence or

scholastic aptitude specifically within South African schools. Claassen et al. (1998) have described

26

the GSAT as an objective aid in determining the reasoning or problem solving ability of school

students. The items in the GSAT provide a good indication of a person’s general intellectual

functioning that is analogous with Spearman’s g factor. Taking into account the theory by Vernon

(1950), Claassen et al. (1998) state that the purpose of the GSAT is largely to determine the verbal-

educational factor (v:ed) rather than the practical mechanical factor (k:m). The GSAT is

consequently classified as a cognitive test of academic ability or scholastic aptitude.

2.1.1.2 Academic ability as a predictor of academic performance

Literature concerning the relation between academic ability or intelligence factors and academic

performance primarily focuses on achievement in specific subjects and not on overall grade

performance. Much of the research has focused on the relationship between academic ability and

performance in Mathematics. Masqud (1983) conducted a study of Mathematics achievement in

Nigerian secondary school students and found a statistically significant positive relationship

between results on the Raven’s Standard Progressive Matrices (RSPM) as a measure of intelligence

and Mathematics achievement scores. In a similar study, Midkiff et al. (1989) investigated the

relationship between general scholastic aptitude and academic performance in a Mathematics

examination and found a significant positive relationship for boys and a moderate positive

relationship for girls. Focusing on black high school students in South Africa, Grobler et al. (2001)

found meaningful positive relations between verbal scholastic aptitude, non-verbal scholastic

aptitude and Mathematics marks for both boys and girls.

With regard to performance in other subjects, Lau and Roeser (2002) conducted research amongst

high school students in the United States of America and found strong correlations (r = 0.67, p <

27

0.01) between scores in a multiple-choice Science test and results of measures of fluid and spatial

abilities. In a university setting, Furnham and Chamorro-Premuzic (2004) correlated students’

grades on a Statistics examination with scores on an intelligence test measuring verbal and visuo-

spatial ability and found meaningful positive relations. Rigdell and Lounsbury (2004) investigated

the influence of cognitive ability on scores in an undergraduate Psychology test as well as students’

grade point averages and found moderate correlations of r = 0.41 (p < 0.01) and r = 0.39 (p < 0.01)

respectively.

While academic ability seems to be an influential factor on academic performance, it is located

within the realm of cognitive functioning. There are other processes and factors affecting academic

performance which can be categorised as non-cognitive functions. One of these non-cognitive

factors is vocational interest, which involves a person’s interest toward participating in activities

and tasks relating to a certain vocation.

2.3 NON-COGNITIVE FACTORS AND ACADEMIC PERFORMANCE

2.3.1 VOCATIONAL INTEREST

The concept of vocational interest has had a profound effect on career development theory and

practice, including high school students who have dreams and aspirations of working in a particular

profession. The most widely recognised linguistic definition of vocational interest is from one of the

pioneers of vocational interest measurement, Edward K. Strong. Crites (1999, pp. 164) cites

Strong’s (1943) definition which compares vocational interests to “tropisms or activities for which

we have liking or disliking and which we go toward and away from, or concerning which we at

28

least continue or discontinue the status quo; furthermore, they may or may not be preferred to other

interests and they may continue over varying intervals of time”.

Super and Crites (1962) suggest four ways in which to operationally define vocational interests: (1)

expressed interests, which is the verbal expression of interest in an object, activity, task or

occupation; (2) manifest interests, which denotes active participation in an activity or occupation;

(3) tested interests, which refers to interests as measured by objective tests, and (4) inventoried

interests, which denotes responses of like, dislike and indifference to verbal presentations of

activities, objects and types of people. In a review of the development of vocational interest theory,

Barak (1981) outlines that theories of interest and interest development were almost all formulated

from the 1930’s to the 1950’s. He classifies the different theories of vocational interest into six

assumptions: (1) Interests are learned; (2) Interests are adjustment modes; (3) Interests are an aspect

of the personality; (4) Interests are an expression of the self-concept; (5) Interests are motives; and

(6) Interests are determined by many factors.

2.3.1.1 Vocational interest, personality and cognitive ability

While Crites (1999) is of the opinion that vocational interests should be separately and uniquely

defined, a number of empirical investigations have provided convincing evidence for some

commonality amongst cognitive ability, vocational interest and personality variables. Rolfhus and

Ackerman (1996) evaluated commonality across verbal and spatial ability, vocational interests and

personality variables and showed a pattern of positive relations between interest in the arts and

humanities, typical intellectual engagement and openness to experience. They also show

correlations between an attainment of Mathematics and Physical Science knowledge and realistic

29

and investigative interests. In a chronological account of his life’s work in vocational interest

measurement, Holland (1999) provides substantial empirical evidence that interest inventories

assess many of the factors entailed in a comprehensive personality inventory. In this regard, De

Fruyt and Mervielde (1999), Barrick et al. (2003), Sullivan and Hansen (2004) and Mount et al.

(2005) have all reported positive relations between vocational interest and personality variables.

In contrast, other studies have revealed little commonality between abilities, interest and personality

constructs. Lowman, Williams and Leeman (1985) measured primary abilities and interest types of

college women and found relatively little common variance between abilities and their

corresponding vocational interests, which suggests that they may be relatively separate domains.

De Bruin (2002) examined correlations between vocational interests as measured by the 19-Field-

Interest Inventory (19FII) and personality factors as measured by the 16 Personality Factor

Questionnaire (16PF) and found that three second–order personality factors, namely Extraversion,

Tough poise and Independence, had weak yet statistically significant relationships with certain

vocational interest fields as measured by the 19FII. His findings suggest that although there may be

some commonality between personality variables and vocational interests, they may represent two

different domains.

While research shows conflicting evidence regarding the relationship between vocational interest

and personality variables, one of the most successful and influential theories describing the

relationship between vocational interests and personality has been the theory of vocational

personalities and work environments by John Holland (1973). Arnold (2004) describes Holland’s

30

theory of vocational choice as being a dominant force in vocational psychology and career

guidance.

2.3.1.2 Holland’s theory of vocational personalities and work environments

Holland’s theory of vocational personalities and work environments was first outlined in 1959 and

has proved to be relatively robust, despite rigorous criticism and subjection to empirical testing. The

theory has been revised and updated many times, however according to the latest (1997) version,

Holland states that people can be characterised into six personality types which resemble their

vocational interests, namely Realistic, Investigative, Artistic, Social, Enterprising and Conventional

(RIASEC). These types can also be descriptive of six model environments, which Holland defines

as the situation or atmosphere created by the people who dominate any given environment.

Holland (1997) describes the personality types according to the RIASEC typologies and outlines

certain preferences and aversions that people may have. Realistic (R) people prefer activities that

entail the explicit, ordered or systematic manipulation of objects, tools, machines and animals and

may have an aversion to educational or therapeutic activities. Typical careers in the realistic

environment would include those in the field of technical or agricultural careers. Investigative (I)

people prefer activities that entail the observational, symbolic, systematic and creative investigation

of physical, biological and cultural phenomena. Investigative people may also have an aversion to

persuasive and repetitive activities. Typical careers in the investigative environment would include

work in the geological or biological sciences as well chemistry and physics. Artistic (A) people

prefer ambiguous, free, unsystematic activities that entail the manipulation of physical, verbal or

human materials to create art forms or products. Artistic people may have an aversion to explicit,

31

systematic or ordered activity. Typical careers in the artistic environment would include a career in

music or photography. Social people (S) prefer activities that entail the manipulation of others to

inform, train, develop, cure or enlighten. They are averse to explicit, ordered, systematic activities

involving materials, tools or machines. Typical careers in the social environment include teaching

or religious ministry. Enterprising people (E) prefer activities that entail the manipulation of others

to attain organisational goals or economic gain. They may be averse to observational, symbolic and

systematic activities. Typical careers in the enterprising environment would include jobs in

marketing or entrepreneurship. Conventional people (C) prefer activities that entail the explicit,

ordered, systematic manipulation of data, for example the keeping of records or filing. They may

have an aversion to ambiguous, free, exploratory or unsystematic activities. Typical careers in the

conventional environment include accounting or secretarial work.

In addition to describing the personality types and work environments, Holland has also organised

them into a working model which he calls a calculus theory. Nel (2006) describes Holland’s

calculus theory whereby the six personality and environment types can be geometrically arranged in

a single hexagonal model so that adjacent types theoretically have more in common than non-

adjacent types. The model is shown in Figure 2.3.

Realistic (R)

Artistic (A)

Investigative (I)

Enterprising (E)

Conventional (C)

Social (S)

Figure 2.3 Hexagonal model illustrating relative distances among personality types

32

2.3.1.3 Measurement of vocational interest

When considering the measurement of vocational interests, Crites (1999) states that the oldest,

continuously used interest inventory currently available is the Strong Interest Inventory (SII). The

SII provides information about a person’s interest in 109 occupations, related to six global types

and 25 basic interests, which represent areas commonly recognised as important for understanding

the organisation and structure of interests as well as the world of work (Hansen, 2000). Another

interest inventory that has had a profound effect on the field of interest measurement is the Kuder

Occupational Interest Survey (KOIS). The purpose of the KOIS is to help young people discover

the occupations they will find most satisfying (Diamond & Zytowski, 2000).

While both the Strong Interest Inventory and the Kuder Occupational Interest Survey are still

currently in use by psychologists specialising in vocational counselling, probably the most

influential and successful instrument for the assessment of vocational interests has been the Self-

Directed Search (SDS) (Holland, 1997). The SDS was developed directly from Holland’s theory

(1973) of personalities and work environments and has become the benchmark for self-guided

career assessment. The SDS and Holland’s theoretical model have provided career assistance to

individuals, groups, and has been utilised in career workshops. The typologies have also been used

to organise and interpret client and occupational information in career centres, libraries and

industrial settings. The questionnaire gauges a person’s resemblance to the six interest or

personality types and work environments. The respondent’s vocational interests are described

according to a three letter code based on the three highest scores on the response sheet. (Spokane &

Catalano, 2000). This code can then be compared to a number of occupational classifications

organised according to the same six categories employed in the assessment questionnaire. In South

33

Africa, the occupational classifications are described in The South African Dictionary of

Occupations (Taljaard & Mollendorf, 1987). Consequently, the respondent can complete the

questionnaire and than search for compatible careers in the dictionary of occupations.

2.3.1.4 Gender and culture differences with respect to vocational interest

Subjective research has been conducted in the field of vocational interest measurement between the

genders, especially regarding interests in Mathematics and Science. For example, O’Brien,

Martinez-Pons and Kopala (1999) analysed the data of 415 eleventh grade school students enrolled

in Mathematics and Science courses which disclosed a direct effect of gender on students’ career

interests. In addition, Kelly (1988) found that boys in secondary schools in the United Kingdom

were more likely than girls to regard studying Physics (Physical Science) as interesting. In a similar

study, Watson, McEwen and Dawson (1994) assessed 1073 secondary students in Northern Ireland

and found that girls rated English, Biology and French as significantly more interesting than boys

did, while boys rated their interest in Physics, Chemistry and Mathematics significantly higher than

girls. With regard to differences in measured interests between the genders, Mullis, Mullis and

Gerwels (1998) compared responses on the Strong-Campbell Interest Inventory (SCII) by male and

female adolescents in the United States of America. They found that males had significantly higher

mean scores on the Realistic category while females had significantly higher scores on the Social

and Conventional categories. Research has also been conducted to investigate gender differences

with regard to Holland’s theory of personalities and work environments. Holland (1972) found that

males and females differ in the arrangement of the six occupational fields organised in the calculus

model and as measured by the Self-Directed Search (SDS). In response to these findings, Feldman

and Meir (1976) conducted research with 322 Israeli female high school students and showed

34

similar results. While males showed the arrangement RIASEC, females were more likely to show

the typological order of IRASEC when considering the occupational fields. In addition, Tuck and

Keeling (1980) conducted research amongst high school students in New Zealand and also found

that the IRASEC arrangement was a slightly better fit for girls.

With regard to cultural differences in high school students’ vocational interests, research has

focused on the validity of Holland’s circular structure of the RIASEC types in different cultures.

Rounds and Tracey (1996) located 96 cross-cultural RIASEC matrices from 19 countries and found

that the cross-cultural equivalence of Holland’s circular order model was not supported. Day and

Rounds (1998) investigated the RIASEC circular structure across ten different racial and ethnic

groups in the United States of America and found a similar underlying structure consistent with

conventional interpretations of vocational interest patterns. Wheeler (1992) conducted a study to

investigate the structural validity of Holland’s circular model amongst black high school students in

South Africa. He found that while the model proved to be valid for black high school students on

the whole, some adjustments needed to be made to the Artistic field as he found the artistic interest

amongst black high school pupils to be more data-oriented and associated more with Conventional

and Enterprising constructs than with Investigative and Social constructs. Also in South Africa, Du

Toit and De Bruin (2002) examined the validity of Holland’s circular order amongst four groups of

Black students. Multidimensional scaling analyses revealed a poor fit for all groups which suggest

that the circular model may not be valid for Black South Africans.

35

2.3.1.5 Vocational interest as a predictor of academic performance

Literature associating measured vocational interests with academic performance in high school

seems to be scarce, however a few empirical studies have investigated this issue. As early as 1968,

Holland conducted a longitudinal study using a sample of college students and found that college

grades for men were not independent of personality types. His theory (1973) speculates that

educational achievement is related to the following personality pattern order: I, S, A, C, E, R and

this speculation is reiterated in the latest version of the theory (1997). This speculation has not been

the object of much empirical investigation, however Schneider and Overton (1983) conducted a

study on college freshman in the United States of America and found that although males and

females with the primary personality types I, S, A and C achieved the highest grade point averages,

the ordering of the groups did not conform with Holland’s prediction. Brown (1994) conducted

research on Engineering students and found that interest variables, when combined with personality

and cognitive factors, accounted for the major portion of variance in predicting first semester grade-

point averages. The most recent and relevant research of the predictive effect of vocational interests

on academic performance appears in Sparfeldt (2007). With a sample of 106 intellectually gifted

adolescents and 98 adolescents of average ability, the researcher concluded that gifted adolescents

displayed higher investigative interests (d = 0.54) and lower social interests (d = 0.38) than non-

gifted adolescents. Differences between both groups regarding their realistic, artistic, enterprising,

and conventional interests were negligible.

2.3.1.6 Influence of vocational interest on educational and occupational pathways

Academic abilities may aid in the development of vocational interests which in turn may shape the

choice of certain educational and vocational pathways. Barak (1981) proposes a model for

36

vocational interests whereby interest in a certain vocation is the result of perceived ability as well as

expected success and anticipated satisfaction. Perceived ability in turn is developed from success in

various activities or experiences relating to the specified career.

According to Social Cognitive Career Theory (SCCT) (Lent et al., 1996), interests are assumed to

be important determinants of career choice. SCCT asserts that self-efficacy expectations and

outcome expectations directly affect the formation of career interests. These emergent interests

promote particular goals for activity involvement and this increases the likelihood that a person will

engage in a particular activity. A diagrammatic representation of the model is presented in Figure

2.4.


choice behaviour

Source: Copyright © 1994 by Lent, R.W., Brown, S.D. & Hackett, G. Reprinted with permission by Jossey-Bass.

As can be seen from the model, according to SCCT, career choices and performance domains and

attainments relating to those choices are affected by a number of variables including a variety of

personal non-cognitive inputs and background contextual factors. It is therefore reasonable to argue

37

that choices of academic subjects and academic performance will be affected by these factors as

well.

2.3.2 PERSON-ENVIRONMENT FIT

2.3.2.1 Holland’s congruence theory

In addition to describing the structure and organisation of vocational interests, Holland (1997)

describes a theory of congruence in which he maintains that a person’s behaviour is determined by

an interaction between personality and the environment (Spokane, Luchetta & Richwine 2002). In a

review of Holland’s congruence theory, Nel (2006) outlines that the degree to which the personality

and the environment match is known as the person-environment fit. He states that people search for

environments that will let them exercise their skills and abilities, express their attitudes and values,

and take on agreeable problems and roles. This has important implications for school students in

that the academic attitudes and study behaviours that influence their academic performance may be

affected by the way in which their personalities correspond with the environment. For example, a

Realistic person in a Realistic environment will experience a higher degree of congruence than a

Realistic person in a Social environment. Holland (1997) states that while teachers are usually S

types, students who do not achieve academically in school are usually R types. These are

incongruent opposites in the hexagonal model and the implication is that in addition to failure to

achieve the minimal skills, R type students may underachieve because they are in an environment

which suits S type personalities. Those students who experience a high degree of congruence

between personality and environment, for example, an S type personality in an S type environment,

may achieve better academic results.

38

2.3.2.2 Person-environment fit and academic performance

There has been little research done on whether the concept of person-environment fit is related to

performance or achievement (Feldman, Smart & Ethington, 1999), however, some research has

been done in higher education settings. Posthuma and Navran (1970) assessed the personalities of

academic staff members and first year students at a military college and found that the highest

academic achievers reflected the most amount of congruence between personality and environment

while the lowest academic achievers reflected the lowest amount of congruence. Reuterfors,

Schneider and Overton (1979) conducted research with first year college students who were either

decided or undecided on their college majors. They found that students with college major choices

in congruence with their personality obtained higher grade-point averages than students who were

incongruent and also undecided on their choice of major. They also found that there was no

significant difference in grade-point average between students of definite and indefinite personality

types who had both decided on college majors. Bruch and Krieshok (1981) conducted research on

students enrolled for an Engineering degree and found that differences in academic performance

were not consistent with Holland’s (1973) congruence hypothesis. Feldman et al. (1999) analysed

differential patterns of change and stability (over a four year period) in the abilities and interests of

congruent and incongruent first year college students. They found that students in congruent fields

(Investigative, Artistic and Enterprising) showed a stronger increase in their dominant skills and

interests over time than incongruent students who showed less of an increase or a decrease in

abilities and interests.

In addition to the relationship between person-environment fit and academic achievement, limited

but important research has been conducted on the relationship between interest and enrolment in

39

school subjects. This can be seen as a measure of person-environment fit in that, according to

Holland (1997), people will choose environments in which they experience the most amount of

congruence. Garrat (1985) and Ainley et al. (1990) both found strong relationships between interest

in a particular subject and enrolment in that subject at high school. Dellar (1994) found that

Australian school students of both high and low ability considered interest in a subject to be a very

important factor. Athanasou (2001) conducted a study on Australian school leavers and found a

strong relationship between interests and enrolment in a particular course in the first year of higher

education.

Holland’s theory implies that vocational interests are not separate constructs in relation to

personality variables and he was of the opinion that factors of intelligence and cognitive ability

were also linked to vocational interests. In some of his earliest work conducting longitudinal studies

on college graduates, Holland (1968) found that college grades for men were not independent of the

personality types and he maintains that cognitive factors, personality variables and vocational

interests share some commonality.

2.3.3 SELF-EFFICACY

The Oxford Dictionary of Psychology (Colman, 2006) defines self-efficacy as a person’s beliefs

about their ability or confidence to bring about intended results. According to Meyer, Moore and

Viljoen (1997), Albert Bandura, one of the most important representatives of Social Cognitive

Learning Theory, is regarded as a pioneer in the study of self-efficacy and its impact on behaviour.

Bandura’s (1986) theory states that self-efficacy perceptions considerably influence a person’s

choice of situation because they will tend to choose situations in which they will achieve success.

40

Consequently, persons with high self-efficacy will produce more success experiences which further

reinforce their self-efficacy while persons with low self-efficacy will produce less successful

experiences thereby reducing their self-efficacy (Meyer et al., 1997). The concept of self-efficacy

has important implications in the study of factors affecting academic performance in high school in

that a student’s perception of subject difficulty may influence whether they are motivated to study

the material and achieve academically in the first place.

Most of the research on self-efficacy has been conducted in the area of academic performance in

Mathematics and Science related subjects. Siegel et al. (1985) found a moderate relationship

between college students’ scores on a Mathematics self-efficacy scale and performance in an

introductory Mathematics course. Also in a university setting, Lent et al. (1991) measured self-

efficacy for Mathematics in 166 Psychology students and found that effects of past achievement

and self-efficacy in Mathematics were useful predictors of Mathematics grades, with effects of past

achievement being partially mediated by self-efficacy. Andrew (1998) found that scores on the

Self-Efficacy for Science Scale could predict 24% of the variance in academic performance in

Physical Science amongst Australian nursing students.

There is also evidence that suggests that self-efficacy in certain activities promotes an interest to

participate in those activities, and this may produce behaviour which increases positive

performance in the activity. Social Cognitive Learning Theory (Bandura, 1986), suggests that

people develop interests in activities in which they view themselves to be efficacious and for which

they anticipate positive outcomes (Lopez, Lent, Brown & Gore, Jr., 1997). Bandura, Barbaranelli,

Caprara and Pastorelli (2001) state that the higher people’s self-efficacy to fulfil educational

41

requirements and occupational roles, the wider the career options they seriously consider pursuing,

the greater interest they have in them, the better they prepare themselves educationally for different

occupational careers, and the greater their staying power in challenging career pursuits. Betz and

Hackett (1983) found that Mathematics self-efficacy expectations were significantly related to the

extent to which students selected Science-based college majors. Lopez et al. (1997) conducted

research on 296 high school students enrolled in advanced Algebra and Geometry courses. They

showed that high school students’ self-efficacy and outcome expectations predicted their interest in

Mathematics and that self-efficacy partially mediated the effect of ability on grades in Mathematics.

They conducted path analysis for their social-cognitive interest and social cognitive performance

models represented in Figure 2.5 and Figure 2.6.

Figure 2.5 Social Cognitive Interest Model

Source: Copyright © 1997 by Lopez et al. Reprinted with permission.

42

Figure 2.6 Social Cognitive Performance Model


In a study amongst 415 eleventh grade high school students, O’Brien et al. (1999) found that career

interest in Science is predicted solely by Science-Mathematics self-efficacy. Lapan, Adams, Turner

and Hinkelman (2000) used cluster analysis to statistically explore interest and efficacy patterns of

seventh grade high school students across Holland’s RIASEC interest themes. They found that boys

who had a high self-efficacy for Enterprising and Artistic careers were also more interested in

pursuing those careers, and boys who had a moderate self-efficacy for Realistic and Investigative

occupations were more interested pursuing those occupations. Turner and Lapan (2002) conducted

regression analysis on high school students’ gender, gender-career typing, career self-efficacy and

career planning/exploration patterns in order to predict their career interests across Holland’s

RIASEC themes. They found that self-efficacy significantly predicted some of the total variance for

all six of Holland’s themes.

43

2.3.4 ACHIEVEMENT MOTIVATION

Achievement motivation, or a person’s need for achievement is described by the Oxford Dictionary

of Psychology (Colman, 2006) as a social form of motivation involving a competitive drive to meet

standards of excellence. Busato et al. (2000) describe achievement motivation as the striving

tendency towards success with the associated positive effects and towards the avoidance of failure

and the associated negative effects and state that it is an important predictor of cognitive

performance.

Achievement motivation and its relationship with academic performance are well documented in

the literature. Busato et al (2000) conducted research with psychology students in the Netherlands

and found that scores on a measure of achievement motivation (Prestatie-Motivatie Test) were

significantly related to academic success, measured in the form of the amount of study points

gained by students at a first, second and third year level. Tavani and Losh (2003) found a

statistically positive relationship between 4012 students’ levels of motivation and their academic

performance as measured by grade point average. Lounsbury et al. (2003) investigated the effect of

work drive, defined as the enduring motivation to spend time and effort to finish projects, meet

deadlines, be productive and achieve success, on academic performance and found a statistically

significant positive relationship.

Wentzel (1989) states that the concept of goal pursuit has been central to the study of achievement

motivation and performance outcomes. She conducted a study on the relationship between high

school students’ single and multiple goals and their academic achievement as indexed by their

grade point averages and found a significant relationship. Results indicated that students with high

44

grade point averages were primarily concerned with the pursuit of social-responsibility and learning

goals while students with low grade-point averages showed more concern for social interaction

goals. In Australia, McInerney, Hinkley, Dowson and Van Etten (1998) investigated the mastery,

performance and social learning goals of Aboriginal, Anglo and Immigrant Australian high school

students. They found that students who embraced a mastery goal-orientation achieved better success

academically and that profiles between cultures were remarkably similar. In a similar study in the

United States of America, Harackiewicz, Tauer, Barron and Elliot (2002) found that performance

goal-orientations were a significant predictor of academic success in a particular subject while

mastery goal-orientation was a significant predictor of continued interest in that subject. In South

Africa, Bosch, Boshoff and Louw (2003) also found that mastery as a goal-orientation had a

significant influence on the academic performance of undergraduate Business Management

students.

Achievement motivation may be partially mediated by other cognitive and non-cognitive factors

that may affect academic performance. Tavani and Losh (2003) state that levels of students’ internal

characteristics, such as motivation and self-confidence strongly influence their achievements during

high school, however little is known concerning the extent to which each of these factors affects

academic performance and expectations. They found that high school students’ motivation was

significantly linked to a drive to achieve as well as leadership ability. In addition, they found that

when students’ levels of motivation were high, so too were their expectations of academic success.

45

2.3.5 COPING STRATEGIES

Students may face many stressors when attempting to achieve in the academic environment. Nonis,

Hudson, Logan and Ford (1998) report that time constraints, financial strain, academic workload

and interpersonal difficulties with lecturers, peers and significant others contribute to stress for

college students. When considering the school environment, many of these stressors may also be

applicable to high school students. Academic performance may depend on students’ utilisation of

resources to effectively develop strategies of coping with academic pressures. Nounopoulos, Ashby

and Gilman (2006) define coping resources as traits, abilities, and assets, both human and material,

that are used to determine subsequent coping strategies. Collins and Onwuegbuzie (2003) define

coping strategies as the cognitive and behavioural tactics that individuals utilise to control their

environmental surroundings and to alleviate any stress which may occur when environmental

demands surpass individuals’ resources. Chu and Choi (2005) describe the three most frequently

mentioned coping strategies in the literature, namely, task-oriented strategies, emotion-oriented

strategies and avoidance-oriented strategies. Task-oriented coping strategies reduce stress by

focusing on immediate problems. Emotion-oriented coping strategies involve diminishing the

emotional distress that is induced by the stressor. Avoidance-oriented strategies involve ignoring a

problem or distracting oneself from it (Chu & Choi, 2005).

A number of empirical studies have investigated different coping strategies and its impact on

academic performance. Zuckerman et al. (1998) investigated self-handicapping as a coping strategy

and its impact on academic performance. Self-handicapping involves the tendency to erect obstacles

to successful performance such as drug and alcohol consumption or choosing debilitating

performance settings in order to protect self-esteem. They found that students high in self-

46

handicapping performed less well academically than students low in self-handicapping and that this

effect was mediated by poor study habits. Nonis et al. (1998) investigated the influence of perceived

control over time as a stress coping strategy amongst college students. They found that high levels

of academic performance were associated with students that perceived high levels of control over

time compared with students who perceived low levels of control over time. Malefo (2000)

investigated the coping strategies of black women in a predominantly white university in South

Africa and found no statistically significant relationship between scores on a measure describing a

broad range of behavioural and cognitive coping strategies and academic performance. Chu and

Choi (2005) conducted research on the effects of procrastination as a coping strategy and its impact

on grade point averages of Canadian university students. They found that students who used

“active” procrastination (a preference to work under pressure) as a coping strategy had higher

grade-point averages than those students who were “passive” procrastinators. Nonopoulos et al.

(2006) investigated the relationship between the coping resources and academic performance of

high school students. They found that confidence in academic pursuits as a coping resource was

positively associated with higher grade-point average. In addition, academic confidence mediated

the relationship between perfectionist tendencies and grade point average.

Apart from its direct effect on academic performance, coping may also indirectly affect certain

learning styles that may impact academic performance. Collins and Onwuegbuzie (2003) studied

two aspects of coping strategy, namely study coping strategies and examination-taking coping

strategies. They found that both of these constructs were statistically significantly related to certain

learning modalities and seem to be a function of learning styles. Educators’ understanding of

47

learning styles can foster an environment more conducive to learning and this may increase


2.3.6 SELF-DIRECTEDNESS IN LEARNING

The concept of self-directedness in learning involves the capacity for an individual to take

responsibility for his or her own learning. Hoban and Hoban (2004) describe self-directed learning

as an elusive construct that may lend itself to a multitude of definitions but which ultimately places

the responsibility for learning on the individual regardless of the method of instruction. Knowles

(1975) defines self-directed learning as a process in which individuals take the initiative to identify

their learning needs, formulate learning goals, identify resources for learning, choose and

implement learning strategies and evaluate learning outcomes. Another definition related to this is

outlined by Costa and Garmston (2001) and Costa and Kallick (2004) who define self-directed

school students as exhibiting self-managing, self-monitoring and self-modifying dispositions of

mind when confronted with complex and sometimes ambiguous and intellectually demanding tasks.

With regard to the influence of self-directedness in learning on academic performance, significant

research has been conducted, the bulk of which suggests that individuals demonstrating high levels

of self-directed learning are more likely to experience success in various learning contexts (Reio,

Jr., 2004). Wall, Hoban and Sersland (1996) found that higher levels of self-directed learning

readiness predicted classroom mathematical performance while Long and Morris (1996) found self-

directed learning readiness to be a useful single-predictor variable of academic success after

controlling for intelligence (Reio, Jr., 2004). A study of the literature also suggests a link between

self-directedness in learning and the concept of self-efficacy in impacting academic performance.

48

This is highlighted in Hoban and Hoban’s (2004) review of Bandura’s (1995) postulates about the

link between self-directed learning, self-efficacy and academic performance. They mention that

Bandura clearly links self-efficacy with self-directed learning in that he states that efficacy beliefs

play an important role in the development of self-directed lifelong school students and that a

student’s belief in their ability to master academic activities affects their academic

accomplishments. Bandura further proposes that students who have a strong belief that they can

regulate their behaviour will also have a strong belief in their ability to master academic

achievements. Expanding on these postulates, Hoban and Hoban (2004) state that a student’s

mastery experiences contributes to raising self-efficacy which, in turn, influences performance and

competence in an almost circular process.

2.3.7 AVOIDANCE OF PROCRASTINATION

Ellis and Knaus (1977) define procrastination as the lack or absence of self-regulated performance

and the behavioural tendency to postpone what is necessary to reach a goal. This act of needlessly

delaying tasks to the point of subjective discomfort is an all-too-familiar problem amongst students

(Solomon & Rothblum, 1984). There is a general consensus amongst researchers and practitioners

that procrastination is a self-handicapping and dysfunctional behaviour and that it may have an

important negative impact on learning and achievement (Solomon & Rothblum, 1984; Wolters,

2003). Chu and Choi (2005) state that procrastination may have serious consequences for students

whose lives are characterised by frequent deadlines. Despite the negative impact that

procrastination may have on academic performance and activity, it is fairly commonplace among

adults as well as students at the high school and college levels (Wolters, 2003).

49

A number of empirical studies have reported the relationship between procrastination and academic

performance. Semb, Glick and Spencer (1979) found that procrastination resulted in detrimental

academic performance, including poor grades and course withdrawl (Solomon & Rothblum, 1984).

Rothblum, Solomon and Murakami (1986) found that self-reported procrastination was negatively

correlated with grade-point average in first year Psychology students and came to the conclusion

that subjects who reported procrastination performed less well academically than did non-

procrastinators. Tice and Baumeister (1997) report that university students who rated high on

procrastination not only received low grades but also reported a high level of stress along with poor

self-rated health. Chu and Choi (2005) distinguish between “active” procrastinators (who prefer to

work under pressure) and “passive” procrastinators defined in the traditional sense. They found that

“passive” procrastinators scored significantly lower on grade point average than “active”

procrastinators and non-procrastinators.

In addition to research on procrastination and its impact on academic performance, studies have

been conducted in order to explain the etiology of procrastination behaviour. Milgram and

Toubiana (1999) tested the appraisal-anxiety-avoidance (AAA) model of procrastination amongst

Israeli high school students. The AAA model proposes that people characterised by fear of failure

about doing certain tasks become anxious when called to perform them and allay their anxiety by

postponing them as much as possible. The researchers found that the more high school students

were anxious about preparing for examinations and writing papers, the more they procrastinated on

these assignments, confirming the AAA model. In a study involving first-year Psychology students,

Wolters (2003) found that procrastination was related to self-efficacy, work-avoidant goal

orientation and students’ metacognitive strategies.

50

2.3.8 CONCLUSION

From a study of the literature, it seems as if the non-cognitive factors reviewed, namely vocational

interest, person-environment fit, self-efficacy, achievement motivation, coping, self-directedness in

learning and avoidance of procrastination, exert a powerful influence on academic performance. By

reviewing the nature of the relationships, it is apparent that the school students who are interested in

their subject fields, whose personalities fit the academic environment, who believe in their ability to

succeed, who are motivated to achieve, who adopt successful coping mechanisms, who are self-

directed in their learning and who avoid procrastinatory activities, will perform better academically

than those who do not show a tendency toward these attitudes and behaviours. Although research

has shown the above mentioned factors to be important non-cognitive predictors of academic

performance, the list is by no means exhaustive. Factors such as locus of control (Masqud, 1983),

beliefs about learning (Cano & Cardel-Elawar, 2004), work ethic (Hill & Rojewski, 1999) and self-

concept (Burns, 1982) have all shown to affect academic performance. It is important to note that,

with the exception of person-environment fit, the above-mentioned factors are intrapsychic in

nature in that they exist within the boundaries of the individual. However, since individuals do not

live in a vacuum and are relational in a sense that they interact with other human beings, it is

important to briefly mention some of contextual factors existing outside of the realm of the

individual that may have an indirect or mediating influence on academic performance.

2.4 CONTEXTUAL FACTORS AFFECTING ACADEMIC

PERFORMANCE

Although not the main aim of this investigation, a study on factors affecting academic performance

that fails to acknowledge the important influence of significant others and socio-cultural

51

phenomena would be incomplete. While a plethora of literature exists on contextual factors, it is

useful to briefly mention two general areas of research, namely the influence of significant others

and socio-cultural factors.

2.4.1 THE INFLUENCE OF SIGNIFICANT OTHERS

Significant research by Kaplan, Liu and Kaplan (2001) showed that parental academic expectations

of their children were positively related to academic performance and a study by Wong, Wiest and

Cusick (2002) indicated that school students’ attachment to their parents increased their motivation

to succeed academically and this consequently impacted their academic performance. They also

showed that a support for a student’s autonomy by teachers was significantly related to an increase

in academic performance.

2.4.2 THE INFLUENCE OF SOCIO-CULTURAL FACTORS

Extensive empirical research has been conducted showing that many socio-cultural factors

influence academic performance. A study conducted in Nigeria by Masqud (1983) showed

significant relations between socio-economic status and academic achievement. A similar study

conducted in South Africa by Masilela (1988) investigated the effects of socio-economic status on

academic achievement and found that through its effect on both school and home environment, it

has a considerable influence. Also in South Africa, Grobler et al. (2001) conducted research with

high school students enrolled in Mathematics courses and showed a significant positive relationship

between level of training in teachers and Mathematics results. They also reported that class size was

negatively related to Mathematics performance in boys. In the United States of America, Zigarelli

(1996) investigated socio-cultural factors pertaining to the school environment as a catalyst for

52

learning. He found that the most important effective school characteristics that promoted school

students who performed well academically were an achievement-oriented school culture, principal

autonomy in hiring and firing teachers and high levels of teacher morale.

2.5 SOCIAL-COGNITIVE CAREER THEORY EXPLAINING

DIFFERENCES IN ACADEMIC PERFORMANCE

Academic performance and choice of subject and career pathways may be partly explained by the

mediating effect of non-cognitive factors such as self-efficacy, vocational interests, motivation and

self-directedness in learning on academic ability. Current theories which explain the link between

cognitive and non-cognitive variables influencing educational/vocational pathways includes Social-

Cognitive Career Theory (SCCT) (Lent et al., 1996), a theory derived from Albert Bandura’s

General Social Cognitive Theory (Bandura, 1986). SCCT attempts to consolidate constructs such as

self-concept, self-efficacy, interests and abilities and composes a comprehensive explanatory

system of the complex connections between persons and their career related contexts (Lent et al.,

2002).

In an explanation of SCCT, Lent et al. (1996) state that ability, as reflected by achievement directly

and indirectly impacts self-efficacy and outcome expectations. High levels of self-efficacy and the

anticipation of valued outcomes promote higher goals which mobilise and sustain performance

behaviour. Consequently, increased performance behaviour should promote educational and

occupational opportunity. Lent et al. (2002) display a performance model whereby a feedback loop

between performance attainments and subsequent behaviour is shown. Success experiences promote

development of abilities and, in turn, self-efficacy and outcome expectations within a dynamic

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cycle. They also note the impact of contextual variables (such as teaching quality, socio-economic

status and gender role socialisation) in the refinement of abilities, self efficacy, outcome

expectations and goals. A diagrammatic representation of the model is shown in Figure 2.7:

Outcome expectations

Ability or past performance

Self-efficacy

Performance goals and sub-goals

Performance Attainment levels

Figure 2.7 Model of Task Performance

Source: Copyright © 1994 by Lent et al. Reprinted with permission by Jossey-Bass.

Bandura et al. (2001) also explain the relationship between ability, self-efficacy and career choices.

They state that the higher people’s level of self-efficacy to fulfil educational requirements and

occupational roles, the wider the career options they seriously consider pursuing, the greater the

interest they have in them, the better they prepare themselves educationally for different

occupational careers, the greater their staying power in challenging career pursuits.

2.6 CHAPTER SUMMARY

The preceding chapter summarises the body of literature relating to the study of some of the non-

cognitive factors affecting academic performance. From the literature review, it can be noted that

while there is a large body of evidence which correlate cognitive factors with academic

performance, non-cognitive factors such as vocational interests, person-environment fit, self-

54

efficacy, achievement motivation, coping skills, self-directedness in learning and the avoidance of

procrastination also have a significant impact on academic performance. How these factors relate to

one another is not clear, however Social Cognitive Career Theory has attempted to provide a

theoretical framework in order to understand how self-efficacy and vocational interest affects task

performance. It seems as if research in this area has mainly been conducted in the workplace and

amongst university students and this warrants further research within a school context. In the next

chapter, the research method employed in this study, is discussed.

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CHAPTER THREE

RESEARCH METHOD

3.1 INTRODUCTION

Chapter Three provides an overview of the research problem and the main aims and purposes of the

study. A comprehensive description of the participants, research instruments, research hypotheses

and research procedure is presented. The chapter concludes with an overview of the statistical

analysis of the data obtained in the study.

3.2 RESEARCH PROBLEM

Academic performance in high school may have a significant influence on the opportunity for entry

into various educational and occupational settings. Developmental theorists such as Super (1990),

Ginzberg (1984) and Gottfredson (1981) have placed an importance on an individual’s academic

experiences during school years in preparing for future academic experiences at a higher education

level as well as occupational opportunities in the workplace (Paa & McWhirter, 2000). The

academic results students achieve within their chosen subjects at a high school level may affect

their educational and occupational pathways with respect to the opportunity to enter into higher

education, selection of study field and major subjects at university or technikon, and their eventual

choice of career. Ainley et al. (1990) states that the subjects studied in the senior secondary years

are a major influence upon the educational and career options available to young people when

leaving school.

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A large body of knowledge exists highlighting the relationship between students’ cognitive ability

and their academic performance (cf. Grobler et al., 2001; Lau & Roser, 2002; Masqud, 1983;

Midkiff et al., 1989; Rigdell & Lounsbury, 2004). In addition, certain non-cognitive factors such as

self-efficacy and achievement motivation, and their relationship with academic performance have

been well researched (cf. Andrew, 1998; Busato et al., 2000; Lent et al., 1994; Siegel et al., 1985;

Tavani & Losh, 2003). However, while evidence suggests that both cognitive and non-cognitive

factors influence academic performance, the relative influence of these factors and their differential

relationships with each other seem to be the subject of much uncertainty (Furnham & Chamorro-

Premuzic, 2004).

One area of research that has received the least amount of attention in this field, especially within a

high school setting in the South African context, is the relationship between students’ vocational

interests and their academic performance. Limited research has shown that school students with

Investigative interests appear to perform better academically and those with Realistic interests do

not perform well academically (cf. Holland, 1973; Schneider & Overton, 1983; Sparfeldt, 2007).

However, there is a possibility that school students who have personalities and interests that match

the content of the subjects they study may perform better academically than students who do not

show a match between personality, interests and subject content. Also, a poor person-environment

fit and a lack of interest in subject material may result in negative academic attitudes and study

behaviours that in turn may hinder academic performance rather than promoting it. This may

consequently influence the opportunity for entry into higher educational settings and various

occupational or vocational fields.

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3.3 PURPOSE OF THE STUDY

The purpose of this study is to provide empirical data on the relationship between vocational

interest and academic performance, when controlling for cognitive academic ability. The results

may supply valuable information to researchers, teachers, parents and high school students on

whether vocational interest should be an important variable to consider when choosing subjects in

high school and whether the relationship between vocational interest and subject choice is an

important predictor of academic success. While Holland’s (1997) theory speculates that educational

achievement is related to the following personality pattern order: Investigative, Social, Artistic,

Conventional, Enterprising, Realistic, little empirical evidence has been gathered to support this

view. In response to the general acceptance of Holland’s (1997) theory, the purpose of this study is

to investigate an alternative possibility, namely that there is a relationship between subject content-

vocational interest fit and academic performance. Specifically, school students experiencing a high

degree of correspondence between vocational interest and subject content will perform better

academically than students experiencing a lower degree of correspondence between vocational

interest and subject content.

In addition, the study investigates the predictive influence of certain academic attitudes and study

behaviours on academic performance. In this regard, the study will provide information about six

other non-cognitive factors that may affect academic performance, namely self-efficacy, person-

environment fit, achievement motivation, coping, self-directedness in learning and procrastination

behaviour. Knowledge of these factors and their impact on academic performance may allow

students to develop positive academic attitudes and study skills, thereby facilitating greater

academic success at high school. Increased academic performance may create more educational and

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occupational opportunity for individuals who want to enter certain career fields but are denied entry

due to low marks at school.

3.4 PARTICIPANTS

The participants consisted of students from a government secondary school in Gauteng, South

Africa with over 1500 students. The school was chosen because it represented a wide range of

school students from different contexts and socio-economic backgrounds. In addition, a large

sample could be drawn from a single grade due to the size of the school. The sample consisted of

285 Grade 10 students, 132 of which were male and 153 of which were female. The average age of

the participants was 16 years. The sample was multicultural with representation from Black (n =

70), Coloured (n = 15), Indian (n = 17) and White (n = 183) racial designations. All Grade 10

students were encouraged to participate in the study but it was explained to them during a parent-

student information evening and in a letter addressed to their parents that participation was

voluntary. Parental consent was obtained before the research was conducted and parents were

assured that all participants would remain anonymous.

3.5 MEASUREMENT INSTRUMENTS

A description of the measurement instruments used, including their purpose, uses in different

contexts and descriptions of the various subtests are highlighted in the following sections. The

reliability and validity of the instruments are also discussed.

59

3.5.1 General Scholastic Aptitude Test (GSAT)

The current study aims to investigate the influence of vocational interest and other non-cognitive

factors on the academic performance of high school students. It was therefore necessary to control

for the cognitive factors, more specifically academic ability, that has been found by other

researchers to strongly predict academic performance. The General Scholastic Aptitude Test

(GSAT) (Senior Series), developed by Claassen et al. (1998) in conjunction with the Human

Sciences Research Council (HSRC), was used as a measure of the academic ability of participants.

The GSAT was selected because it was developed specifically for South African high school

students and can be administered in a group context.

The GSAT measures various reasoning and problem-solving abilities associated with academic

performance in a high school setting. Although the test comprises of a variety of item types, it

specifically measures cognitive academic ability and does not aim to provide a differentiated picture

of a broad range of intellectual functioning (Claassen et al., 1998). The GSAT (Senior Series) was

developed for Afrikaans-speaking and English-speaking South African school students from the age

of 13 years and 6 months to 18 years. Three versions of the test are available, namely a full version,

a shortened version and a shortened speeded version in which the time for completion of the

subtests is limited. The GSAT can be administered in English or Afrikaans and two alternate forms

(Forms A and B) of equal difficulty and with one set of norms between them are available. For

purposes of this research, the shortened speeded version of the test was used as it was able to elicit

reliable information about general scholastic aptitude as well as verbal and non-verbal intelligence

factors, and it was also able to fit within the limited time constraints that were set out by the

60

authorities at the school. Since all of the participants had English as their language of instruction,

Forms A and B of the English version were used.

3.5.1.1 Uses of the GSAT

The GSAT can be used as an objective aid in determining an individual’s general level of academic

ability in order to guide and direct educators with regard to the academic abilities of their students.

It has been used as a screening test for admission to secondary schooling and university and as an

objective measure to determine the placement of students in special programmes such as those for

gifted school students or those with intellectual limitations.

3.5.1.2 Description of the subtests

The shortened speeded version of the GSAT (Senior Series) consists of four subtests, two each of

which provide information about the verbal and non-verbal academic abilities of a student. The

subtests which make up the verbal academic ability component include:

• Subtest 1 : Word Analogies – a measure of the ability to observe the relation between two

words and to use this relation to complete another word pair by analogy as an aspect of

verbal reasoning ability.

• Subtest 3 : Verbal Reasoning – a measure of the ability to determine relations, form new

concepts and manipulate them in a logical manner as an aspect of verbal reasoning ability.

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The subtests which make up the non-verbal academic ability component include:

• Subtest 2 : Number Series – a measure of the ability to determine relations between numbers

in a series, to deduce the rule applicable to a particular number series and apply it to

complete the number series. This provides a good measure of non-verbal reasoning ability.

• Subtest 4 : Pattern Completion – a measure of the ability to observe figures accurately, to

determine the relation between the figures and apply the rules to complete the patterns. This

provides a measure of non-verbal reasoning ability.

3.5.1.3 Reliability and validity of the GSAT

The GSAT has proved to be both a reliable and valid measure of academic ability within a South

African context. Jooste (2004) highlights that the reliability test-retest coefficients of the test vary

between 0.84 and 0.96. In terms of the content and construct validity, this has been fairly well-

established with a fair predictive validity coefficient of 0.54 for scholastic achievement (Jooste,

2004). Predictive validity for academic performance in examinations has also been established for

the GSAT. Table 3.1 shows research by Claassen et al. (1998) in which high scores on the

shortened version of the GSAT were positively correlated with final examination marks for

Standard 7 (Grade 9) students.

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Table 3.1 Correlation coefficients for shortened GSAT and examination marks

Final Exam Subject N Correlation (r)

Afrikaans (1st language) 462 0.67

Afrikaans (2nd language) 280 0.49

English (1st language) 296 0.52

English (2nd language) 448 0.64

Mathematics 725 0.64

Physical Science 733 0.60

History 715 0.50

Geography 724 0.57

Typing 206 0.43

3.5.2 Self-Directed Search (SDS)

The current study aims to investigate the relationship between vocational interest and academic

performance. It was therefore necessary to include an objective measure of vocational interest. The

Self-Directed Search (SDS), developed by John Holland in 1985 was chosen as a measure of

vocational interest because of evidence for its cross-cultural validity as well as its reliability and

applicability in the field of career psychology.

3.5.2.1 Purpose of the SDS

The SDS is a measure of vocational interest and personality type that was developed according to

Holland’s (1973) theory of personality type and work environments. It also provides information

about a person’s career orientations and aims to establish a correlation between the personal aspects

63

of individual and career information. The items of the SDS measure a preference for certain

activities, the skills they are familiar or competent in, their interest in a variety of occupations and

their assessment of their own abilities. Results reveal information categorised according to six

occupational themes, namely Realistic, Investigative, Artistic, Social, Enterprising and

Conventional (RIASEC).

3.5.2.2 Uses of the SDS

The SDS has been used with high school students, university students and adults and can be used as

an aid in a number of different contexts, including the determination of vocational interests for

career counselling purposes. It has also been used in the selection, placement and occupation

classification in business and industry as well as for investigating alternate career possibilities with

the aid of occupational codes. Additionally, it has assisted in the determination of an individual’s

personal development with more than one administration over time (Nel, 2006).


The SDS comprises of four sections, each of which measures the six RIASEC interest fields. The

four sections are described according to:

1. Activities: This subtest comprises of 66 items which represent the six (RIASEC) interest fields.

The respondent indicates his or her interest in a variety of activities in the workplace.

2. Competencies: This subtest comprises of 66 items which represent the six (RIASEC) interest

fields. The respondent indicates whether he or she has a working knowledge of an activity or is

competent in a particular activity.

64

3. Occupations: This subtest comprises of 84 items, which represent the six (RIASEC) interest

fields. The respondent indicates his or her feelings and attitudes toward a variety of occupations.

4. Self-rating of abilities or skills: This section consists of two groups (I and II), each comprising

of six abilities or skills correlating with the six (RIASEC) interest fields. The respondent uses a

six-point scale to rate his or her mechanical, scientific, artistic, teaching, sales and clerical

abilities.

With regard to this study, only the total scores for the SDS were used. This score is calculated from

the subscales mentioned above.

3.5.2.4 Reliability and validity of the SDS

Gevers, Du Toit and Harilall (1997) report that there have been a number of studies in which the

SDS was used in South Africa. Of particular importance to the current research is a study that was

conducted in 1997 which aimed to adapt the SDS for use in the South African context. Using the

latest version, the same as used in this study, the SDS was administered to 4573 Standard 7 (Grade

9) and Standard 9 (Grade 11) high school students from English, Afrikaans, Nguni and Sotho

backgrounds (Gevers et al., 1987). The coefficients of reliability were computed and are displayed

in Table 3.2.

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Table 3.2 Reliability coefficients for SDS adaptation study (Sichel formula)

SDS fields Reliability Coefficients

Realistic 0.88

Investigative 0.85

Artistic 0.87

Social 0.85

Enterprising 0.77

Conventional 0.82

Regarding the validity of the SDS, the results of the item analysis of the 1997 South African study

supported the content validity of the questionnaire. The structural relationship between the SDS

fields were also determined and results confirmed the structure of occupational interest as defined

by Holland whereby he states that adjacent fields have more in common (RI, IA, AS, SE, EC, CR)

while opposite fields have less in common (RS, IE, AC) (Gevers et al., 1997). Table 3.3 shows the

intercorrelations of the SDS fields from the 1987 study.

Table 3.3 Intercorrelations of the SDS fields (1987 study, N = 4573)

RI IA AS SE EC CR Adjacent

fields 0.32 0.30 0.60 0.59 0.62 0.24

RA IS AE SC ER CI Alternate

fields 0.12 0.33 0.44 0.48 0.27 0.33

RS IE AC Opposite

fields 0.04 0.32 0.33

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With regard to the current study, Table 3.4 shows the reliability statistics, estimated by means of

Cronbachs alpha, for the SDS subtests.

Table 3.4 SDS reliability statistics for a sample of high school students (N=285)

SDS Interest Field Cronbach’s Alpha Number of items

Realistic 0.905 38

Investigative 0.893 38

Artistic 0.890 38

Social 0.889 38

Enterprising 0.885 38

Conventional 0.866 38

From Table 3.4 it is clear that the subtests of the SDS yielded satisfactory reliabilities for the

sample in the current study.

3.5.3 Academic Behaviours and Attitudes Questionnaire (ABAQ)

The study aims to investigate additional non-cognitive factors besides vocational interest that may

influence academic performance. It was therefore necessary to include a measure which could elicit

information about other important determinants of academic performance.

3.5.3.1 Purpose of the ABAQ

The ABAQ was developed based on a need to understand non-cognitive aspects that account for

differences in the academic achievement of students at institutions of higher learning (De Bruin, De

Bruin, Schoeman & Hardy, 2005). The ABAQ measures six non-cognitive aspects related to the

67

academic behaviour and attitudes of students, which are represented in the following subtests,

namely Self-efficacy expectations, Person-environment fit, Achievement motivation, Social coping,

Self-directedness in learning, and Academic procrastination.

3.5.3.2 Uses of the ABAQ

The ABAQ was originally developed to be administered in university settings, however, for

purposes of the present study, some of the items were adjusted for high school students. The

instrument provides useful information to students, parents, teachers and guidance counsellors on

some of the attitudes and behaviours that high academic achievers may be adopting to facilitate

their success. It may also highlight areas of concern for those individuals that are academically

under-achieving.


The ABAQ comprises of 60 individual items which are divided into six subtests which represent the

constructs of Self-efficacy, Person-environment fit, Achievement motivation, Coping, Self-directed

learning and Avoidance of procrastination respectively (De Bruin et al., 2005). Respondents rate

their attitudes and behaviours on a five point Likert-type scale ranging from Strongly Agree to

Strongly Disagree.

3.5.3.4 Reliability and validity of the ABAQ

De Bruin et al. (2005) report the reliability and validity for the ABAQ for university populations.

Table 3.5 shows test-retest reliability coefficients of the ABAQ subscales between genders and

across different language groups using Cronbach’s coefficient alpha:

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Table 3.5 Reliability coefficients for the ABAQ across language and gender groups

Afrikaans English Sotho Nguni Men WomenABAQ subscale

n=724 n=1020 n=427 n=299 n=1432 n=1771

Self efficacy 0.82 0.83 0.80 0.81 0.82 0.84

Person-environment fit 0.86 0.87 0.84 0.82 0.86 0.86

Achievement Motivation 0.77 0.76 0.63 0.65 0.74 0.74

Coping 0.78 0.77 0.73 0.71 0.76 0.80

Self-directedness in learning 0.68 0.70 0.58 0.65 0.65 0.66

Avoidance of Procrastination 0.87 0.88 0.86 0.86 0.88 0.87

In terms of the validity of the instrument, De Bruin et al. (2005) explored the validity of the ABAQ

in predicting performance in a first year university Economics examination across racial

designations. They state that the ABAQ scales jointly accounted for approximately eight percent of

the variance of the examination scores for White respondents and three percent for Black

respondents. They also explored the validity of the ABAQ in predicting academic performance after

controlling for mental ability using the General Scholastic Aptitude Test (GSAT) and found that the

GSAT accounted for approximately 20 percent of the variance in an Economics examination while

the ABAQ accounted for a further 15 percent of the variance in a sample of 136 respondents.

Cronbach’s alpha coefficients of the six subscales for the participants in the current study are

reported as follows: Self-efficacy (α = 0.85), Person-environment fit (α = 0.83), Achievement

69

motivation (α = 0.76), Coping (α = 0.74), Self-directed learning (α = 0.67) and Avoidance of

procrastination (α = 0.84). Not all of these coefficients appear to be acceptable (α <= 0.70),

however they all compare well to the coefficients reported by De Bruin et al. (2005).

3.6 PROCEDURE

When considering that most of the participants in this study were school students under the age of

16 years, care was taken to ensure that the participants, their parents, teachers and other significant

role-players were fully aware of the research which was to be conducted. Firstly, permission was

obtained by the Principal and Head of Department: Life Orientation for the research to be

conducted at the school. Special arrangements were made with the four Grade 10 Life Orientation

teachers to utilise a period of teaching time for the administration of the psychometric instruments.

A full brief regarding the research project was given by the researcher at a Grade 10 parents

information evening. Prospective participants, their parents and teachers were informed about the

nature of the project, the procedures that would be followed with regard to psychometric evaluation,

the implications for the participants during the time of research and the benefits of participation. It

was explained that participation was entirely voluntary and that results would be made available as

soon as the data was analysed and reported.

Following the information evening, students were given a letter which further explained the

procedures and implications of the research project as well as the right to refuse participation. It was

discussed in the letter that parents needed to give consent to the researcher in order for their child to

70

participate. It was explained to both students and parents that individual results of the psychometric

instruments would not be disclosed to anyone.

Once the consent forms had been gathered from the parents, the researcher arranged two time slots

during which the participants completed the psychometric instruments. The first instrument to be

completed was the Self-Directed Search (SDS) which was done on a class-by-class basis during a

Life Orientation lesson. The researcher and a registered psychometrist administered the

questionnaire over eleven sessions with classes of approximately 30 respondents each. At the

beginning of each session students were asked if there were any one of them who decided not to

participate. The students who indicated that they do not want to take part in the study, were not

required to complete the questionnaire.

During the second session the General Scholastic Aptitude Test (GSAT) and the Academic

Attitudes and Behaviours Questionnaire (ABAQ) were administered. This involved gathering all

the participants together in the school hall and gym with the support of the teachers and the Grade

10 tutors. The researcher and a registered psychologist administered these instruments. Desks were

spaced far enough apart to prevent plagiarism and a fifteen minute break was given in between the

administration of the GSAT and ABAQ to prevent any confounding factors such as fatigue or lack

of concentration.

Following the completion of the psychometric instruments, the results of all the Grade 10 school

students for the mid-year and final examination were obtained from the school administrator with

permission from the Principal and Head of Department: Life Orientation. These results were

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combined into an averaged result for each student. The psychometric instruments were scored by

the researcher and captured by the Statistical Consultation Services (STATCON) at the University

of Johannesburg. Once the data was captured, the researcher analysed the data in consultation with

STATCON. Following the initial data analysis, school students and parents were informed about

the findings which were reproduced in a document distributed to each Grade 10 learner.

3.7 HYPOTHESES

3.7.1 Research Hypothesis 1

It is hypothesised that academic ability, as measured by the General Scholastic Aptitude Test, has a

statistically significant relationship with academic performance. More specifically, it is

hypothesised that as academic ability increases, the overall academic result will also increase.


It is hypothesised that school students who show vocational interest patterns that correspond with

specific subject content, perform academically better than school students who do not have interests

that are in line with the content of the subjects they study. Specifically, those school students who

have vocational interest patterns that have much in common with the content of a specified subject,

will achieve higher academic results as represented by their overall examination percentage in that

subject, when controlling for academic ability.


It is hypothesised that school students who show positive academic attitudes and study behaviours

perform academically better than students who show negative academic attitudes and study

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behaviours. Specifically, those school students who show higher scores with respect to self-

efficacy, person-environment fit, achievement motivation, coping, self-directedness in learning and

avoidance of procrastination will achieve higher academic results, as reflected by their overall

averaged result, than those school students who show lower scores with respect to self-efficacy,

person-environment fit, achievement motivation, coping, self-directedness in learning and

avoidance of procrastination.

3.8 STATISTICAL ANALYSIS

The research design employed in this study is a non-experimental survey design, intended to

provide information about the relationships between vocational interest and other non-cognitive

factors affecting academic performance. In accordance with the stated research problem and

purpose of the study, the data analysis was divided into three sections, namely:

(a) The analysis of the data pertaining to Hypothesis 1: Investigating the relationship between

cognitive ability and academic performance.

(b) The analysis of the data pertaining to Hypothesis 2: Investigating the relationships between

vocational interests and academic performance in subjects that correspond with the specified

interests.

(c) The analysis of the data pertaining to Hypothesis 3: Investigating the relationship between non-

cognitive factors and academic performance.

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The statistical analysis of the data was performed using the Statistical Package for Social Sciences

(SPSS, version 14). Descriptive and inferential statistics were employed in the analysis of the data

of this study. The statistical methods selected in the analysis of the data are discussed below.

3.8.1 Descriptive statistics

To describe the sample, frequency distributions were utilised for categorical variables such as

gender, racial designation and language group. Frequency distributions were also used to report on

continuous variables such as age, academic results and cognitive ability as well as the participants’

predominant vocational interests, academic attitudes and study behaviours. The minimum and

maximum values as well as the means and standard deviations are provided for these variables.

Cronbach’s alpha coefficients were computed for each of Holland’s interest categories on the SDS

as well as for the six dimensions of the ABAQ, as already reported in sections 3.5.2.4. and 3.5.3.4

respectively.

3.8.2 Inferential statistics pertaining to Hypothesis 1

Hypothesis 1 of the study involved describing the relationship between an independent predictor

variable, namely cognitive ability, and a continuous dependent variable, namely academic

performance. A statistical method that has proved useful in studying a relationship of this nature is a

simple linear regression model (Field, 2005). In simple regression analysis, variable X is used to

predict or explain variable Y and can be described by the formula y = bx + c (Miles & Shevlin,

2001). For the present study, the predictor variable (x) was cognitive ability and the dependent

variable (y) was academic performance. The significant level of the relationship was considered at

the p < 0.05 and p < 0.01 level.

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3.8.2 Inferential statistics pertaining to Hypotheses 2 and 3

Hypotheses 2 and 3 of the study involve describing the relationship between more than one

independent predictor variable and a continuous dependent variable in the presence of a moderator

variable. For Hypothesis 2, this would describe the relationship between vocational interest and

academic performance while controlling for the moderating effect of cognitive ability. A useful

procedure that allows for the description of these relationships is hierarchical multiple regression

analysis (Field, 2005). In hierarchical multiple regression analysis, more than one independent

variable (X1, X2) are used to predict or explain variable Y and can be described by the equation Y

= b1x1 + b2x2 + c (Miles & Shevlin, 2001). Multiple regression can tell us how good the prediction

is and how much of the variance of Y is accounted for by the linear combination of the independent

variables (Field, 2005). The significant level of the relationship was considered at the p < 0.05 and

p < 0.01 level.

It is important to note that the independent variables are entered into the equation based on

theoretical orientation. For the purposes of this study, the theoretical orientation applied is Social

Cognitive Career Theory which states that performance is a function of non-cognitive factors such

as vocational interest while taking into account the mediating effect of cognitive ability. Therefore,

cognitive ability will be entered into the hierarchy of the equation as the first predictor variable,

followed by the non-cognitive factors. Considering that Hypothesis 2 involves describing whether a

vocational interest that is related to a certain subject increases academic performance, two

regression equations for each of the subjects will be computed. Firstly, vocational interests that are

expected to be related to a particular subject based on Holland’s (1997) theory (for example

Investigative interests and Mathematics), will be entered into the multiple regression equation. The

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amount of variance explained by vocational interest with regard to academic performance while

controlling for academic ability will be recorded. Thereafter the remaining vocational interests will

be entered into the first multiple regression equation to establish whether any additional variance

can be explained.

For Hypothesis 3, the relationship between certain academic attitudes and study behaviours, and

academic performance will be described while controlling for the moderating effect of cognitive

ability.

3.9 CHAPTER SUMMARY

This chapter summarises the research method adopted in the study. It would seem that school

students who disregard the importance of non-cognitive factors perform poorer academically. The

purpose of the study was to investigate the relationship between certain non-cognitive factors and

academic performance, with a specific focus on vocational interests. Two hundred and eighty five

Grade 10 students from diverse backgrounds completed a number of instruments to measure

academic ability, vocational interests and certain academic attitudes and study skills. The data was

analysed using a series of multiple regression techniques, the results of which are reported in the

next chapter.

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CHAPTER FOUR

RESULTS

4.1 INTRODUCTION

Chapter Four provides an overview of the results of the study in terms of descriptive and inferential

statistics. All statistical procedures were performed using SPSS (version 14). The results are

reported as they relate to the various hypotheses stated in Chapter Three.

4.2 DESCRIPTIVE STATISTICS

The sample yielded a total of 285 Grade 10 participants who consisted of males and females

between the ages of 13 and 18 from across four different racial designations and ten language

groups. Tables 4.1, 4.2, 4.3 and 4.4 provide information regarding age, gender, race and language

variables.

Table 4.1 Age statistics for sample of 285 Grade 10 students

Age Frequency Percent Cumulative Percent 13 1 0.4 0.4 14 9 3.2 3.5 15 197 69.1 72.6 16 67 23.5 96.1 17 10 3.5 99.6 18 1 0.4 100.0 Total 285 100.0

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Table 4.1 shows that the majority of the sample (69.1%) consisted of individuals who were 15 years

old. The next most representative age group was 16 years (23.5%), followed by 17 years (3.5%), 14

years (3.2%) and 18 years (0.4%) respectively.

Table 4.2 Gender statistics for sample of 285 Grade 10 students

Gender Frequency Percent Cumulative Percent Male 132 46.3 46.3 Female 153 53.7 100.0 Total 285 100.0

With regard to gender, Table 4.2 shows the distribution of males and females to be fairly even, with

132 males (46.3%) and 153 females (53.7%) being represented.

Table 4.3 Racial designation statistics for sample of 285 Grade 10 students

Racial Designation Frequency Percent Cumulative Percent Black 70 24.6 24.6 White 183 64.2 88.8 Asian/Indian 17 6.0 94.7 Coloured 15 5.3 100.0 Total 285 100.0

Table 4.3 shows that the majority of the sample (64.2%) was from a White racial background. The

next most represented racial designation was Black (24.6%), followed by Asian/Indian (6%) and

Coloured (5.3%).

78

Table 4.4 Home language statistics for sample of 285 Grade 10 students

Frequency Percent Cumulative Percent North Sotho 10 3.5 3.5 South Sotho 2 0.7 4.2 Tswana 18 6.3 10.5 Tsonga 2 0.7 11.2 Venda 1 0.4 11.6 Xhosa 12 4.2 15.8 Zulu 17 6.0 21.8 Afrikaans 4 1.4 23.2 English 208 73.0 96.1 Other 11 3.9 100.0 Total 285 100.0

With regard to home language, the descriptive statistics show that the majority of the sample was

English speaking (73%), followed by Tswana (6.3%), Zulu (6%), Xhosa (4.2%), other languages

(3.9%), North Sotho (3.5%), Afrikaans (1.4%), South Sotho (0.7%), Tsonga (0.7%) and Venda

(0.4%).

4.3 RESULTS PERTAINING TO HYPOTHESIS 1

Hypothesis 1 stated that cognitive ability, as measured by the General Scholastic Aptitude Test, has

a statistically significant relationship with school students’ average academic performance. The

variables that apply to Hypothesis 1 were obtained by using the average academic results and the

academic ability score (as measured by the GSAT) for each participant. The average academic

result is comprised of mid-year and final examination marks from all the school subjects taken. A

simple linear regression using SPSS was performed to investigate the relationship between

academic ability and overall academic performance. The results of this analysis are presented in

Table 4.5.

79

Table 4.5 Predictive effect of academic ability on overall academic performance

Model R R2 Adjusted

R2 Change Statistics

R2

Change F

Change df1 df2 Sig. F Change 1 0.542(a) 0.294 0.292 0.294 118.019 1 283 0.000 a Predictors: (Constant), Academic ability

As presented in Table 4.5, the results show a significant positive relationship between academic

ability and overall academic performance. With academic ability as the only predictor of academic

performance, R2 = 0.294, F(1, 283) = 118.019, p < 0.001. Academic ability explained

approximately 29% of the variance in the school students’ overall academic performance. On the

basis of these results, Hypothesis 1 can be regarded as true for this particular sample.


Hypothesis 2 stated that school students’ who show vocational interest patterns that correspond with

specific subject content, perform academically better than learners who do not have interests that

are in line with the content of the subjects they study. To investigate Hypothesis 2, the average

marks of the mid-year and final examinations for six individual school subjects as well as academic

ability and vocational interests were taken into account. The school subjects represented were

chosen on the basis that they represented Holland’s RIASEC interest groups while taking into

account the sample size for each subject, those with small sample sizes being omitted. Each subject

was designated one or more fields of vocational interests in which the content of the field of interest

was regarded as being a good fit with the content of the school subject. Interests were assigned to

the subjects on the basis of Holland’s (1997) descriptions of the vocational preferences of the

RIASEC personality types. Table 4.6 shows the subjects considered as part of this research as well

80

as the corresponding fields of vocational interests assigned on the basis of correspondence with

subject content.

Table 4.6 Subjects considered in study with corresponding vocational interests

Subject N Relevant vocational interest

Accounting 90 Conventional

Business Economics 125 Enterprising

English 277 Artistic, Social

Life Orientation 277 Social

Life Sciences 113 Investigative

Mathematics 162 Investigative

A series of hierarchical multiple regression analyses was performed in which the various

independent variables were entered into the regression equation in a hierarchical fashion in order to

predict the dependent variable, namely subject-specific academic performance. Firstly, academic

ability was entered into the equation as a control variable (or the first predictor/independent

variable). Secondly, the vocational interest variable that corresponds with the specific school

subject was entered to establish if it does in fact contribute to any additional variance over and

above academic ability. Finally, the remainder of the RIASEC interest fields were entered, to

establish whether any additional variance was explained. The total contributions for each step in the

equation as well as the part or unique contribution of the variables were considered.

81

4.4.1 Results pertaining to Accounting

A hierarchical multiple regression analysis was performed with academic performance in

Accounting as dependent variable and academic ability and the six vocational interest fields as

independent variables. Academic ability was entered into the regression equation first, followed by

the Conventional interest, and then the remaining five vocational interests. The results are

summarised in Table 4.7.

Table 4.7 Predictive effect of vocational interests on academic performance in Accounting

Model R R2 Adjusted

R2 Change Statistics

R2 Change F Change df1 df2 Sig. F

Change 1 .445(a) .198 .189 .198 22.184 1 90 .001 2 .502(b) .252 .235 .054 6.397 1 89 .013 3 .610(c) .372 .320 .121 3.229 5 84 .010 a Predictors: Academic ability b Predictors: Academic ability, C c Predictors: Academic ability, C, A, R, I, E, S

With academic ability as the only predictor, R2 = 0.198, F(1, 90) = 22.184, p < 0.001. The

Conventional interest explained a further 5.4% of the variance in Accounting performance, ΔR2 =

0.054, F(1, 89) = 6.397, p < 0.013. The remaining five vocational interest fields jointly explained a

further 12.1% of the variance in performance in Accounting, ΔR2 = 0.121, F(5, 84) = 3.229, p <

0.01. Jointly, academic ability and the six interests accounted for 37.2% of the variance in

Accounting. The standardised regression weights, t-values, p-levels and semi-partial correlations of

the predictor variables with academic performance in Accounting are summarised in Table 4.8.

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performance in Accounting

Model StandardisedCoefficients t p Correlations

ß Zero-order Partial Part

1 Academic ability .445 4.710 .001 .445 .445 .445


C .232 2.529 .013 .214 .259 .232


C .199 1.847 .068 .214 .198 .160 R -.166 -1.836 .070 -.055 -.196 -.159 I .366 3.434 .001 .467 .351 .297 A -.068 -.666 .507 .031 -.072 -.058 S .033 .281 .780 .194 .031 .024 E -.136 -1.191 .237 .027 -.129 -.103 a Dependent Variable: Accounting mark

Inspection of Table 4.8 shows that in step three of the hierarchical analysis, only the Investigative

interest (β = 0.366, r = 0.297, t = 3.434, p < 0.001) and academic ability (β = 0.335, r = 0.307, t =

3.547, p < 0.001) were significantly related to Accounting in the presence of all the remaining

interests. However, the hierarchical analysis has shown that, as expected, the Conventional interest

does explain a significant portion of the variance in Accounting above and beyond that explained by

academic ability.

4.4.2 Results pertaining to Business Economics

A hierarchical multiple regression analysis was performed with academic performance in Business

Economics as dependent variable and academic ability and the six vocational interest fields as


83

the Enterprising interest, and then the remaining five vocational interests. The results are


Table 4.9 Predictive effects of vocational interests on academic performance in Business

Economics

Model R R2 Adjusted R2 Change Statistics

R2

Change F Change df1 df2 Sig. F

Change 1 .323(a) .104 .097 .104 14.562 1 125 .001 2 .370(b) .137 .123 .033 4.693 1 124 .032 3 .539(c) .290 .248 .153 5.128 5 119 .001

a Predictors: Academic ability b Predictors: Academic ability, E c Predictors: Academic ability, E, R, I, A, C, S With academic ability as the only predictor, R2 = 0.104, F(1, 125) = 14.562, p < 0.001. The

Enterprising interest explained a further 3.3% of the variance in Business Economics performance,

ΔR2 = 0.033, F(1, 124) = 4.693, p < 0.032. The remaining five vocational interest fields jointly

explained a further 15.3% of the variance in performance in Business Economics, ΔR2 = 0.153, F(5,

119) = 5.128, p < 0.001. Jointly, academic ability and the six interests accounted for 29% of the

variance in Business Economics. The standardised regression weights, t-values, p-levels and semi-

partial correlations of the predictor variables with academic performance in Business Economics

are summarised in Table 4.10.

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performance in Business Economics

Model Standardised Coefficients t p Correlations




E .182 2.166 .032 .222 .191 .181 3 Academic

ability .242 3.031 .003 .323 .268 .234

E .073 .693 .489 .222 .063 .054 R -.228 -2.672 .009 -.137 -.238 -.206 I .291 3.286 .001 .387 .288 .254 A .070 .739 .462 .234 .068 .057 S .008 .081 .936 .268 .007 .006 C .128 1.322 .189 .260 .120 .102

a Dependent Variable: Business Economics mark

Inspection of Table 4.10 shows that in step three of the hierarchical analysis, the Investigative

interest (β = 0.291, r = 0.254, t = 3.286, p < 0.001), Realistic interest (β = -0.228, r = -0.206, t = -

2.672, p < 0.009) and academic ability (β = 0.242, r = 0.234, t = 3.031, p < 0.003) were

significantly related to Business Economics in the presence of all the remaining interests. However,

the hierarchical analysis has shown that, as expected, the Enterprising interest does explain a

significant portion of the variance in Business Economics above and beyond that explained by

academic ability.

4.4.3 Results pertaining to English

A hierarchical multiple regression analysis was performed with academic performance in English as

dependent variable and academic ability and the six vocational interest fields as independent

85

variables. Academic ability was entered into the regression equation first, followed by the Social

and Artistic interests, and then the remaining four vocational interests. The results are summarised

in Table 4.11.

Table 4.11 Predictive effects of vocational interests on academic performance in English


R2


Change 1 .526(a) .277 .274 .277 106.109 1 277 .001 2 .564(b) .318 .311 .041 8.360 2 275 .001 3 .696(c) .484 .471 .166 21.792 4 271 .001

a Predictors: Academic ability b Predictors: Academic ability, S, A c Predictors: Academic ability, S, A, R, C, I, E

With academic ability as the only predictor, R2 = 0.277, F(1, 277) = 106.109, p < 0.001. The Social

and Artistic interests explained a further 4.1% of the variance in English performance, ΔR2 = 0.041,

F(2, 275) = 8.360, p < 0.001. The remaining four vocational interest fields jointly explained a

further 16.6% of the variance in performance in English, ΔR2 = 0.166, F(4, 271) = 21.792, p <

0.001. Jointly, academic ability and the six interests accounted for 48.4% of the variance in English.

The standardised regression weights, t-values, p-levels and semi-partial correlations of the predictor

variables with academic performance in English are summarised in Table 4.12.

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performance in English

Model Standardised Coefficients t p

Correlations




S .199 3.419 .001 .227 .202 .170 A .009 .153 .878 .151 .009 .008


S .119 2.003 .046 .227 .121 .087 A -.008 -.152 .880 .151 -.009 -.007 R -.262 -5.391 .001 -.238 -.311 -.235 I .349 6.838 .001 .500 .384 .298 E -.115 -1.907 .058 -.050 -.115 -.083 C .077 1.370 .172 .100 .083 .060

a Dependent Variable: English mark



5.391, p < 0.001), Social interest (β = 0.119, r = 0.087, t = 2.003, p < 0.046) and academic ability (β

= 0.396, r = 0.360, t = 8.242, p < 0.001) were significantly related to English in the presence of all

the remaining interests. It should be noted that the effect of the Artistic interest became non-

significant when reviewing the semi-partial correlations of the interest groups. However, the

hierarchical analysis has shown that, as expected, both the Social and Artistic interests do explain a

significant portion of the variance in English above and beyond that explained by academic ability.

87

4.4.4 Results pertaining to Life Orientation

A hierarchical multiple regression analysis was performed with academic performance in Life

Orientation as dependent variable and academic ability and the six vocational interest fields as


the Social interest, and then the remaining five vocational interests. The results are summarised in

Table 4.13.


Orientation


R2


Change 1 .444(a) .197 .194 .197 67.873 1 277 .001 2 .476(b) .226 .221 .029 10.493 1 276 .001 3 .655(c) .429 .414 .203 19.241 5 271 .001

a Predictors: Academic ability b Predictors: Academic ability, S c Predictors: Academic ability, S, R, C, I, A, E

With academic ability as the only predictor, R2 = 0.197, F(1, 277) = 67.873, p < 0.001. The Social

interest explained a further 2.9% of the variance in Life Orientation performance, ΔR2 = 0.029, F(1,

276) = 10.493, p < 0.001. The remaining five vocational interest fields jointly explained a further

20.3% of the variance in performance in Life Orientation, ΔR2 = 0.203, F(5, 271) = 19.241, p <

0.001. Jointly, academic ability and the six interests accounted for 42.9% of the variance in Life

Orientation. The standardised regression weights, t-values, p-levels and semi-partial correlations of

the predictor variables with academic performance in Life Orientation are summarised in Table

4.14.

88


performance in Life Orientation


ß Zero- order Partial Part



S .172 3.239 .001 .191 .191 .172


S .035 .558 .577 .191 .034 .026 R -.331 -6.478 .001 -.278 -.366 -.297 I .373 6.945 .001 .477 .389 .319 A -.006 -.104 .917 .127 -.006 -.005 E -.057 -.901 .368 -.026 -.055 -.041 C .096 1.627 .105 .132 .098 .075

a Dependent Variable: Life Orientation mark




significantly related to Life Orientation in the presence of all the remaining interests. It should be

noted that the effect of the Social interest became non-significant when reviewing the semi-partial

correlations of the interest groups. However, the hierarchical analysis has shown that, as expected,

the Social interest does explain a significant portion of the variance in Life Orientation above and

beyond that explained by academic ability.

89

4.4.5 Results pertaining to Life Sciences

A hierarchical multiple regression analysis was performed with academic performance in Life

Sciences as dependent variable and academic ability and the six vocational interest fields as


the Investigative interest, and then the remaining five vocational interests. The results are



Sciences

Change Statistics Model

R

R2

Adjusted R2


Change 1 .532(a) .283 .276 .283 44.521 1 113 .001 2 .671(b) .451 .441 .168 34.255 1 112 .001 3 .736(c) .542 .512 .092 4.292 5 107 .001

a Predictors: Academic ability b Predictors: Academic ability, I c Predictors: Academic ability, I, S, R, C, A, E


Investigative interest explained a further 16.8% of the variance in Life Sciences performance, ΔR2 =

0.168, F(1, 112) = 34.255, p < 0.001. The remaining five vocational interest fields jointly explained

a further 9.2% of the variance in performance in Life Sciences, ΔR2 = 0.292, F(5, 107) = 4.292, p <

0.001. Jointly, academic ability and the six interests accounted for 54.2% of the variance in Life

Sciences. The standardised regression weights, t-values, p-levels and semi-partial correlations of the

predictor variables with academic performance in Life Sciences are summarised in Table 4.16.

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performance in Life Sciences

Model StandardisedCoefficients t p Correlations




I .457 5.853 .001 .603 .484 .410


I .526 6.798 .001 .603 .549 .445 R -.129 -1.630 .106 -.061 -.156 -.107 A -.127 -1.652 .101 -.055 -.158 -.108 S .096 1.077 .284 -.005 .104 .070 E -.253 -2.562 .012 -.135 -.240 -.168 C .097 1.139 .257 .119 .109 .074

a Dependent Variable: Life Sciences mark Inspection of Table 4.16 shows that in step three of the hierarchical analysis, the Investigative

interest (β = 0.526, r = 0.445, t = 6.798, p < 0.001), Enterprising interest (β = -0.253, r = -0.168, t =

-2.562, p < 0.012) and academic ability (β = 0.310, r = 0.277, t = 4.234, p < 0.001) were

significantly related to Life Sciences in the presence of all the remaining interests.

4.4.6 Results pertaining to Mathematics

A hierarchical multiple regression analysis was performed with academic performance in

Mathematics as dependent variable and academic ability and the six vocational interest fields as


the Investigative interest, and then the remaining five vocational interests. The results are


91


Mathematics


R

R2

Adjusted R2


Change 1 .327(a) .107 .102 .107 19.438 1 162 .001 2 .371(b) .138 .127 .030 5.687 1 161 .018 3 .473(c) .224 .189 .087 3.479 5 156 .005

a Predictors: Academic ability b Predictors: Academic ability, I c Predictors: Academic ability, I, R, E, A, C, S


Investigative interest explained a further 3.0% of the variance in Mathematics performance, ΔR2 =

0.030, F(1, 161) = 5.687, p < 0.018. The remaining five vocational interest fields jointly explained a

further 8.7% of the variance in performance in Mathematics, ΔR2 = 0.087, F(5, 156) = 3.479, p <

0.005. Jointly, academic ability and the six interests accounted for 18.9% of the variance in

Mathematics. The standardised regression weights, t-values, p-levels and semi-partial correlations

of the predictor variables with academic performance in Mathematics are summarised in Table

4.18.

92


performance in Mathematics





I .183 2.385 .018 .263 .185 .175


I .204 2.600 .010 .263 .204 .183 R -.107 -1.360 .176 -.100 -.108 -.096 A -.073 -.871 .385 -.076 -.070 -.061 S -.093 -.983 .327 -.029 -.078 -.069 E -.191 -1.958 .052 -.136 -.155 -.138 C .268 2.882 .005 .101 .225 .203

a Dependent Variable: Mathematics mark


interest (β = 0.204, r = 0.183, t = 2.600, p < 0.010), Conventional interest (β = 0.268, r = 0.203, t =


significantly related to Mathematics in the presence of all the remaining interests.

It would seem that for all the subjects considered, Hypothesis 2 can be accepted in that vocational

interests which fit with specific subject content explain a meaningful proportion of the variance in

academic performance over and above what is explained by cognitive ability. It should be noted

however that vocational interests that did not correspond with subject specific content also

explained a proportion of the variance in academic performance, particularly the Investigative and

Realistic interest. More will be discussed about this in Chapter Five.

93


Hypothesis 3 stated that learners who show positive academic attitudes and study behaviours


behaviours. The results that apply to Hypothesis 3 were obtained by using average academic results

over all the subjects taken (mid-year and final examination marks), academic ability (as measured

by the GSAT) and the total scores for the six dimension of the ABAQ. A multiple regression

analysis was conducted to investigate the relationship between the various academic attitudes and

study behaviours and overall academic performance while controlling for academic ability. The

results of the analysis are presented in Table 4.19.

Table 4.19 Predictive effects of ABAQ factors on overall academic performance


R

R2

Adjusted R2


Change 1 .542(a) .294 .292 .294 118.019 1 283 .001 2 .672(b) .452 .438 .158 13.284 6 277 .001

a Predictors: Academic ability b Predictors: Academic ability, Person-environment fit, Coping, Avoidance of procrastination, Self-efficacy, Self-directed learning, Achievement motivation

With academic ability as the only predictor, R2 = 0.294, F(1, 283) = 118.019, p < 0.001. The ABAQ

factors explained a further 15.8% of the variance in overall academic performance, ΔR2 = 0.158,

F(1, 277) = 13.284, p<0.001. Jointly, academic ability and the ABAQ factors accounted for 45.2%

of the variance in overall academic performance. The standardised regression weights, t-values, p-

levels and semi-partial correlations of the predictor variables with overall academic performance

are summarised in Table 4.20.

94

Table 4.20 Regression weights, t-tests and effect sizes pertaining to the predictive effects of

ABAQ factors on overall academic performance


ß Zero-order Partial Part 1 Academic ability .542 10.864 .001 .542 .542 .542 2 Academic ability .422 8.756 .001 .542 .466 .389 SE .238 3.885 .001 .482 .227 .173 PE -.038 -.707 .480 .177 -.042 -.031 AM .177 2.725 .007 .329 .162 .121 SD .172 2.859 .005 .395 .169 .127 CO .019 .401 .688 .081 .024 .018 AP -.145 -2.401 .017 .090 -.143 -.107

Note: SE = Self-Efficacy, PE = Person-environment fit, AM = Achievement motivation, SD = Self-directed learning, CO = Coping, AP = Avoidance of procrastination

Inspection of Table 4.20 shows that in step two of the hierarchical analysis, Academic ability (β =

0.422, r = 0.389, t = 8.756, p < 0.001), Self-efficacy (β = 0.238, r = 0.173, t = 3.885, p < 0.001),

Achievement motivation (β = 0.177, r = 0.121, t = 2.725, p < 0.007), Self-directedness in learning

(β = 0.172, r = 0.127, t = 2.859, p < 0.005) and Avoidance of procrastination (β = -0.145, r = -

0.107, t = -2.401, p < 0.017) were significantly related to overall academic performance in the

presence of all the remaining ABAQ factors.

4.6 CHAPTER SUMMARY

Chapter Four presented the results of the study that correspond with the stated research hypotheses.

With respect to Hypothesis 1, as expected, it was found that academic ability significantly

contributed to the variance in overall academic performance. For Hypothesis 2, the results showed,

as expected, that interest groups that correspond with subject specific content also contribute

towards the variance in subject specific academic performance over and above what is explained by

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academic ability. In addition however, it was unexpected to find that Investigative and Realistic

interests were associated with positive and negative academic performance respectively, despite a

low fit with specific subject content. With regard to Hypothesis 3 the results showed, as expected,

that certain academic attitudes and behaviours significantly contributed to overall academic

performance over and above academic ability. These results are discussed in Chapter Five.

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CHAPTER FIVE

DISCUSSION OF FINDINGS, LIMITATIONS OF THE STUDY AND

IMPLICATIONS FOR FURTHER RESEARCH

5.1 INTRODUCTION

In this chapter the results of the current research on vocational interests and other non-cognitive

factors affecting academic performance are discussed. Inferences as to what factors may have

specifically affected the outcomes of the study are also highlighted. In addition, an alternative

theoretical framework to Social Cognitive Career Theory for describing factors influencing task

performance is suggested for the high school context. The chapter also deals with the implications

of the results for the various groups which may be affected by the research and limitations of the

study are discussed. Recommendations are made for further research topics based on the study’s

findings and limitations.

The study aimed to provide information about various non-cognitive factors that affect the

academic performance of high school students. Research in this area is important as school

students’ academic results at a high school level are instrumental in shaping their educational and

career pathways. A lack of information about the importance of non-cognitive factors such as

vocational interest, self-efficacy and achievement motivation, negative academic attitudes and study

behaviours, and a pervasive belief amongst school students that intelligence is the predominant

factor affecting academic performance may contribute to poor academic performance. Poor

academic performance denies opportunities for further education and training and consequently

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closes doors on various occupational opportunities which may have been available. The goals of the

study were to provide objective information about the role of various non-cognitive factors,

specifically vocational interest, person-environment fit, self-efficacy, achievement motivation,

coping, self-directedness in learning and avoidance of procrastination in relation to academic

performance. This information could be used by school students and their significant others to

facilitate decisions and programmes aimed at improving academic performance at high school. A

number of research hypotheses were formulated about factors affecting academic performance.

Firstly, it was hypothesised that cognitive factors would contribute significantly toward the variance

in academic performance. Secondly, it was hypothesised that school students who had vocational

interests that correspond with the subjects they are enrolled for would perform better academically

than those school students whose interests do not correspond with their subjects. Lastly, it was

hypothesised that school students who show positive academic attitudes and study behaviours will


behaviours. Simple and multiple regression analyses were used to determine which factors

significantly predicted academic performance and what the nature of the prediction was. The

following sections discuss the results of the findings.

5.2 VARIABLES AFFECTING ACADEMIC PERFORMANCE

5.2.1 Academic ability

The relationship between academic ability and academic performance was investigated by means of

a simple regression analysis. The results of this analysis showed that academic ability accounted for

more than 29% of the variance in overall academic performance. This finding seems to be

consistent with other studies (cf. Furnham & Chamorro-Premuzic, 2004; Grobler et al., 2001; Lau

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& Roeser, 2002; Masqud, 1983; Midkiff et al., 1989; Rigdell & Lounsbury, 2004) which also report

significant positive relationships between academic ability and academic performance, particularly

in the area of Mathematics.

It is interesting to note those subjects in which academic ability appears to play a smaller or almost

negligible role. The effect of academic ability on academic performance in these subjects is lower

than the effect of academic ability on overall academic performance, as measured by an average of

all the subjects taken. This seems to be the case for Accounting, Business Economics, Life

Orientation and Mathematics in which the multiple correlation coefficients (R2) were found to be

0.198 (p < 0.001), 0.104 (p < 0.001), 0.197 (p < 0.001) and 0.102 (p < 0.001) respectively (see

Tables 4.7, 4.9, 4.13 and 4.17 respectively). The relationship between academic ability and overall

academic performance where the correlation coefficient was found to be 0.294, appears to be

meaningfully stronger.

With regard to Mathematics, the smaller effect size of academic ability on academic performance

(see Table 4.17) does not seem to be consistent with the literature (Grobler et al., 2001; Midkiff et

al., 1989) which reports significant stronger multiple correlation coefficients. These researchers

found strong correlations among general scholastic aptitude, academic achievement, and

examination performance, with the highest correlations among these variables ranging from 0.69 to

0.74. A reason for the smaller effect of academic ability on academic performance in this sample

could possibly be related to the fact that school students are streamed into one of two Mathematics

subjects, namely Mathematics and Mathematics Literacy. If a student does not achieve a certain

aggregate by the end of Grade 9, he or she is required to do Mathematics Literacy in which the

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content requires less cognitive ability and focuses on functional Mathematics problems. It could be

that the variability in academic ability in school students enrolled for Mathematics is not large due

to the fact that the seemingly more intelligent school students are being streamed into the subject. It

is recommended that further research be done in this regard because various non-cognitive factors

might have a mediating effect on academic performance, thereby influencing the streaming process.

With regard to the Business Economics and Life Orientation results, it is possible that success in

these subjects relies on experiential learning and common sense as well as academic ability. The

subject content is such that the school students may be exposed to the various concepts and learning

material in everyday life and this experiential learning factor may decrease the influence of

academic ability which is more cognitive in nature. The small effect of academic ability on

Accounting performance was unexpected as the subject does require a certain amount of planning

and organisational skills that are expected to be present in individuals with higher cognitive ability.

However, Accounting at a school level does have a large “practice” component in that once a

method is learned, it can be applied to the question to formulate a result. It seems that it does not

require much of the abstract thinking component of academic ability as opposed to subjects such as

English.

5.2.2 Vocational Interest

As mentioned in the chapter introduction, multiple regression analysis was used to investigate the

relationship between vocational interest and academic performance. As was hypothesised, the

results indicated that school students with vocational interests that correspond with specific subject

content, performed better than the students who did not show this high level of fit between interests

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and subjects. This was found to be the case for all the subjects considered, namely, Accounting

(R2= 0.054; p < 0.013), Business Economics (R2= 0.123; p < 0.032), English (R2= 0.318; p <

0.001), Life Orientation (R2= 0.226; p < 0.001), Life Sciences (R2= 0.451; p < 0.001) and

Mathematics (R2= 0.138; p < 0.018). Therefore one can expect that students who have specific

career goals and who are interested in certain types of work may perform better academically when

the subjects that they study relate in some way to their vocational interests. The study showed that

school students’ interests were not independent of achievement, contradicting findings by Ainley et

al. (1990) in a longitudinal study of Australian school students in which no relationship was found.

Even though the results of the present study support what was expected according to Hypothesis 2,

it should be taken into account that only a small amount of variance in academic performance was

explained by the vocational interests which seemed to fit with subject specific content. Most of the

subject specific vocational interests explained less than 6% of the variance in academic

performance, with the exception of Life Sciences in which 16.8% of the variance was explained. In

hypothesising why the contribution of subject specific vocational interests is not higher, it is of

particular interest to note the vocational interests that had a large impact on academic performance,

regardless of whether they correspond with the subject content or not. Specifically, Investigative

interests were significantly and positively associated with performance in Accounting (r = 0.297; p

< 0.001), Business Economics (r = 0.254; p < 0.001), English (r = 0.298; p < 0.001), Life

Orientation (r = 0.319; p < 0.001), Life Sciences (r = 0.445; p < 0.001) and Mathematics (r = 0.183;

p < 0.010). Realistic interests also had a significant negative relationship with academic

performance in Business Economics (r = -0.206; p < 0.009), English (r = -0.235; p < 0.001) and

Life Orientation (r = -0.297; p < 0.001), a total of three out of the six subjects considered. This

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suggests that school students who have an interest in observational, symbolic, systematic and

creative activities involving physical, biological and cultural phenomena may perform better

academically, whether they are interested in the subject or not and whether their interests

correspond to the subject content or not. In contrast, those school students who prefer Realistic

interests and activities that entail the explicit, ordered or systematic manipulation of objects, tools,

machines and animals and who may have an aversion to educational or therapeutic activities, may

not perform academically well, whether they are interested in the subject or not. These findings are

consistent with Holland’s (1997) theory that school students with Investigative interests appear to

perform better academically and those with Realistic interests do not perform well academically.

This finding is also supported by research done by Schneider and Overton (1983) and Sparfeldt

(2007). It seems therefore that in this study, Holland’s (1997) theories about academic performance

appear to be fairly robust.

It is interesting to note the trends in the data when the influence of all the vocational interests on

academic performance are considered, such as in step three of the multiple regression analysis (see

Tables 4.7, 4.9, 4.11 & 4.13 respectively). Conventional, Enterprising and Social interests

significantly explained some of the variance in Accounting, Business Economics, English and Life

Orientation academic performance respectively, when entered into the regression equations on their

own. However this effect seems to be moderated by Investigative and Realistic interests and the

impact of the other subject specific interests becomes of no significance, with the exception of Life

Sciences and Mathematics in which the subject specific interest was Investigative at the outset.

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In attempting to explain why the presence of Investigative and Realistic interests have such an

effect on academic performance, one could speculate that the academic culture of high schools is

suited to individuals with Investigative interests who enjoy researching into their subject matter.

Their scientific approach to class work and homework tasks may be suited to the way in which

lessons are presented and also may be congruent with the interests of the teachers. In contrast, those

with Realistic interests are more practical and hands-on and therefore the classroom style of

teaching evident in most academic high schools may not be in line with their specific interests. This

finding seems to be consistent with research reported by Posthuma and Navran (1970). They

assessed the personalities of academic staff members and first year students at a military college

and found that the highest academic achievers reflected the most amount of congruence between

personality and environment while the lowest academic achievers reflected the lowest amount of

congruence. Schneider and Overton (1993) also state that educational achievement may be related

to the type of environment, which Holland believes to be defined in part by the situation or

atmosphere created by the people who dominate the environment. It could be argued that this

relates to the construct of person-environment fit measured by the ABAQ, however the ABAQ

factor relates more to whether an individual fits with the subject or courses that they have chosen

and does not relate to the general environment as defined by the interests of the people that occupy

it.

It is interesting to note that in one of the scientific subjects, specifically Life Sciences, there appears

to be a negative correlation between Enterprising vocational interests and academic performance (r

= -0.168; p < 0.012). This supports Holland’s (1997) congruence theory whereby people search for

environments that will let them exercise their skills and abilities, express their attitudes and values,

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and take on agreeable problems and roles. He also states that certain personality types will avoid

certain kinds of work and working environments. Holland describes Enterprising individuals as

people who prefer activities that entail the manipulation of others to attain organisational goals or

economic gain and that they may be averse to observational, symbolic and systematic activities.

Observational, symbolic and systematic activities are characteristic of the sciences and school

students who are averse to them may not perform as well academically in science-related subjects.

Another significant and rather unexpected finding was the positive relationship between

Conventional interests and academic performance in Mathematics. According to Holland (1997),

Conventional individuals prefer activities that entail the explicit, ordered, systematic manipulation

of data and they tend to be very organised and systematic. One may attempt to explain the positive

relationship between Conventional interests and Mathematics from the point of view that the

mentioned Conventional attributes may facilitate performance in Mathematics, especially at a

Grade 10 level where there is more emphasis on mathematical procedures and less of a creative or

artistic component.

5.2.3 Academic attitudes and study behaviours

Multiple regression analysis was used to investigate the relationship between the various academic

attitudes, study behaviours and academic performance. The results showed that three of the six

ABAQ factors have meaningful positive relationships with overall academic performance, namely

Self-efficacy, Achievement motivation and Self-directedness in learning. A significant negative

relationship emerged between Avoidance of procrastination and academic performance, while no

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significant relationship between Person-environment fit and performance as well as Coping and

performance could be found.

5.2.3.1 Self-efficacy and academic performance

The findings on the relationship between Self-efficacy and academic performance suggest that this

factor plays an important role in facilitating academic performance. This seems consistent with

Bandura’s (1986) theory that persons with high levels of self-efficacy will produce more success

experiences which in turn reinforce their level of self-efficacy. On the contrary, persons with low

levels of self-efficacy will produce less successful experiences, thereby reducing their self-efficacy

levels (Meyer et al., 1997). The research also seems consistent with a number of quantitative studies

(cf. Andrew, 1998; Lent et al., 1994; Siegel et al., 1985) in which it has been reported that self-

efficacy is positively related to academic performance. As mentioned in Chapter Two, self-efficacy

is a key tenet of Social Cognitive Career Theory (SCCT) and, within this theory, is linked to aspects

of vocational interest. Specifically, SCCT suggests that people develop interests in activities in

which they view themselves to be efficacious and for which they anticipate positive outcomes

(Lopez et al., 1997). In the present study, both vocational interests and self-efficacy were positively

related to academic performance and so seems to be consistent with the social-cognitive interest

model of SCCT. However, the SCCT model may need to be revised for this study considering that

Investigative and Realistic interests had such a profound effect on academic performance. The

formulation of an adjusted explanatory model for this particular sample is discussed in section 5.3

of this chapter and it is recommended that further research be conducted in similar populations and

other contexts to verify the research data.

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5.2.3.2 Person-environment fit and academic performance

With regard to Person-environment fit, the non-significant relationship between this factor and

academic performance was another unexpected finding. A positive relationship between person-

environment fit and academic performance could have contributed to the explanation for the strong

positive effect of the Investigative interest and the strong negative affect of the Realistic interest on

academic performance as mentioned in section 5.2.2, however no significant relationship was

found. It could be that the Person-environment fit factor may be more appropriate for university

students because they have more of a choice regarding faculty or subjects and because the

environments of particular faculties differ, as opposed to a school where the general environment

remains homogenous for the entire group. The ABAQ was originally designed for use in a

university setting and so this factor may need to be revised further to accommodate high school

environments.

5.2.3.3 Achievement motivation and academic performance

As expected, Achievement motivation was positively associated with academic performance. This

finding is supported by studies conducted by Busato et al. (2000), Tavani and Losh (2003) and

Lounsbury et al. (2003). These results suggest that school students with a striving tendency towards

success including the associated positive effects, and a striving towards the avoidance of failure and

the associated negative effects, perform better academically. This may relate to parental influence

and the expectation that parents set for their children to achieve academically. If a child performs

well academically, there may be positive results such as praise or a material reward which in turn

fosters additional motivation. In contrast, poor academic performance may result in negative

outcomes such as punishment. This is in line with research reported by Kaplan et al. (2001) where it

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has been shown that parental academic expectations of their children were positively related to


5.2.3.4 Self-directedness in learning, Coping and academic performance

The results showed that Self-directedness in learning was significantly and positively related to

academic performance. This suggests that school students who exhibit a high degree of self-

management and self-monitoring when confronted with a complex or ambiguous task perform

academically better than those students who do not show these qualities. It is interesting to note that

the Coping factor of the ABAQ was not significantly related to academic performance at a high

school level. The Coping factor on the ABAQ is related to an individual’s ability to gain social

support from significant others to assist in improving their academic performance. The research

shows that in this sample, tactics used to gain social support with the aim to alleviate stresses does

not seem to have an impact on their academic performance. A possibility for this could be that

social support is unavailable due to a number of factors. With the ever increasing emphasis on

administrative duties in the education field and large class sizes, teachers may be too busy to offer

school students individual attention. In addition, the trend towards both caregivers working a full

day may mean that academic support from parents is unavailable to school students. It seems as

though in this study, self management is more important in facilitating good grades than coping

strategies which aim to elicit social support. This may be due to the nature of the education system

in which the students are working in. Because most school students are minors, they experience

little control over how much work they are given by their teachers and their power of negotiation is

limited. Also, most teachers have a set curriculum and a set amount of work which needs to be

completed, and they may not be receptive to modifying the syllabus in order to assist poor academic

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performers. This makes it increasingly difficult for some school students to initiate coping

mechanisms with the intent to modify the external environment so that they can manage their

workload. Those students who manage themselves despite the environmental pressures, in other

words, a more self-directed learner, are more likely to succeed academically in a school

environment.

5.2.3.5 Avoidance of procrastination and academic performance

With regard to avoidance of procrastination, the results were quite unexpected. While it was

predicted that avoidance of procrastination would be significantly associated with academic

performance, it was expected that the less school students engaged in procrastinatory activities, the

better they would perform academically. However, the results showed an inverse relationship

between avoidance of procrastination and academic performance, implying that the more school

students procrastinated, the better they performed academically. This seems inconsistent with the

body of research on procrastination behaviour (cf. Rothblum et al., 1986; Semb et al., 1979;

Solomon & Rothblum, 1984; Tice & Baumeister, 1997; Wolters, 2003) which has noted the adverse

effects or procrastinatory behaviour on academic performance as well as high levels of stress and

poor self-rated health. It is possible that in this study, better academic performance could be as a

result of “active procrastination”, a concept described by Chu and Choi (2005) in which students

will procrastinate so that they will work hard under pressure, thereby facilitating an increase in

academic performance. In view of the fact that a positive relationship between avoidance of

procrastination and academic performance has been extensively reported in the literature, the

current research results pertaining to this factor are treated with a high degree of scepticism and it is

suggested that further research be performed to validate these unusual findings.

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5.3 A NEW EXPLANATORY MODEL FOR ACADEMIC PERFORMANCE

OF HIGH SCHOOL STUDENTS IN THE RESEARCH SAMPLE

As has been discussed in Chapter Four and in the above sections, both self-efficacy and vocational

interests were significantly related to academic performance in the current sample. The results seem

to be consistent with the Social Cognitive Interest and Performance Models of Lopez et al. (1997)

outlined in Chapter Two. According to the Social Cognitive Interest Model, Lopez et al. (1997)

showed that high school students’ self-efficacy and outcome expectations predicted their interest in

Mathematics and that self-efficacy partially mediated the effect of ability on grades in Mathematics.

These models are shown again in Figure 5.1 and 5.2.

Figure 5.1 Social Cognitive Interest Model


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Figure 5.2 Social Cognitive Performance Model


When reviewing the above models, it is evident that self-efficacy influences vocational interest and

academic performance separately, however SCCT maintains that the direct predictive effect of self-

efficacy and vocational interests on academic performance occurs concurrently. This is explained

further in the section below.

The results of the current study also seem consistent with the Social Cognitive Career Theory (Lent

et al., 1996) and the model of person, contextual and experiential factors affecting career related

choice behaviour (Lent et al., 1994), also outlined in Chapter Two. According this model, person

inputs and background contextual factors have an impact on learning experiences which affects a

person’s self-efficacy and outcome expectations. This in turn affects a person’s interests, choice

goals, choice actions and ultimately, performance levels. A diagrammatic representation of the

theory is depicted in Figure 5.3.

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choice behaviour

Source: Copyright © 1994 by Lent et al. Reprinted with permission by Jossey-Bass.

When reviewing the above model, the vocational interest factor which has been seen to be

important when considering the current study is not divided into general vocational interests and

subject specific vocational interests. Considering the significant effect of subject specific vocational

interests and Investigative and Realistic interests in this sample, it is evident that this model needs

to be revised to explain the academic performance of the high school students in the current study.

Taking this into account, it is proposed that self-efficacy expectations in turn has an effect on both

subject-specific vocational interests and Investigative and Realistic interests, and this in turn affects

a school student’s choice goals, choice actions and academic performance. This model would also

accommodate Holland’s (1997) theory that the presence of Investigative and the absence of

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Realistic interests promotes academic performance. The suggested model is represented in Figure

5.4.

Person inputs - Predispositions - Gender - Race/ethnicity - Disability or health status

Background contextual factors

Learning experiences

Self-efficacy

Outcome expectations

Subject-specific vocational interests

Choice goals

Choice actions

Performance domains and attainment Realistic and

Investigative vocational interests

Contextual influences proximal choice behaviours

Figure 5.4 Explanatory model for academic performance in high school students

From the above model it should be seen that self-efficacy, subject-specific vocational interests and

Investigative and Realistic interests all may have an impact on performance goals directly, thereby

facilitating academic performance. In this model, a school student’s belief in their ability to perform

a certain type of task or job reinforces their interest in the vocations related to those tasks. However

the way in which interests influence academic performance in specific subjects may be regarded as

a two-fold process. The first process involves the presence of two specific interests, namely

Investigative and Realistic interests. According to the model, the presence of these two interests

will affect academic performance in any subject and is not related to subject-specific content. This

part of the theory is in line with Holland’s (1997) theory of vocational interests and academic

performance. The second process involves subject-specific vocational interests. A school student’s

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belief in their ability to perform a specific task will promote particular vocational interests that

promote better academic performance in specific subjects related to those vocational interests. No

formal data analysis was conducted on the relationship between self-efficacy and vocational

interests in this sample, therefore it is recommended that further research be conducted to

empirically validate the proposed model for the broader population of high school students.

5.4 IMPLICATIONS OF THE RESEARCH FINDINGS

The current study has a number of implications for school students who are attempting to better

their academic performance. Firstly, the study shows that non-cognitive factors have an important

part to play in facilitating good grades, dispelling the myth amongst some high school students that

intelligence or academic ability is the only factor affecting academic performance. School students

who take cognisance of the fact that their vocational interests, academic attitudes and study

behaviours affect their academic results may adopt more positive attitudes and behaviours,

facilitating an increase in performance. Of equal importance is the issue of subject choice. School

students who choose subjects in line with their vocational interests have a greater chance of

succeeding academically and therefore this should be an important consideration when selecting

subjects at the end of their Grade 9 year.

An important implication for parents is to encourage the development of Investigative interests, as

this seems to foster good academic performance in a high school setting. In contrast however, those

students with strong Realistic interests may be at a disadvantage in that perhaps they are not suited

to the environment and teaching style associated with an academic focus in high school. Parents,

teachers and guidance counsellors need to be aware that students who are Realistic in their

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vocational orientation may learn differently and might need to be accommodated in terms of

teaching style within the academic environment. Although Outcomes Based Education in South

Africa has implemented policies which aims to transform education into a more hands-on and

experiential type of learning, more needs to be done to accommodate the variety of vocational

personality types in a school setting.

Parents, teachers and guidance counsellors may also facilitate better academic attitudes and study

behaviours by encouraging school students to believe in their ability to achieve at their optimal

level, thereby increasing a student’s self-efficacy. Reward systems could be revised, not only for the

top achievers but for all students who consistently improve their academic performance.

Recognition of academic performance is a positive stimulus which may increase achievement

motivation to strive for higher grades. Encouragement and education pertaining to a student’s level

of self-directedness should also be encouraged, focusing on the student's ability to manage himself

or herself independently when faced with various academic tasks.

5.5 LIMITATIONS OF THE STUDY AND IMPLICATIONS FOR FUTURE

RESEARCH

The study has a number of limitations which need to be taken into consideration when reviewing

the findings. Firstly, according to strict empirical guidelines, the participants should be drawn at

random from the population, whereas in the case of this study, participants from one high school

were used. A sample from a single school needed to be used because the same examination results

for each school subject were required to assess academic performance. However, this does not

enable the study to be generalised to other populations of high school students. The proposed

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adjusted model of academic performance therefore applies only to the current sample and its

applicability to the population of high school students in South Africa needs to be investigated

further. Secondly, it would be more appropriate to incorporate larger sample sizes per subject,

specifically for Accounting which only had 90 participants. Small sample sizes reduce the statistical

power of the hierarchical regression analysis.

The study also brought up a number of topics and problems that may be the focus of further

research, especially in a South African context. For example, the effect of academic ability and non-

cognitive factors on Mathematics performance needs to be researched, especially considering the

introduction of Mathematics Literacy and the streaming of students into these two subjects based on

their academic performance in Grade 9 Mathematics. This topic should be the subject of further

study as school students who have the ability to perform well academically in Mathematics may be

streamed into Mathematics Literacy classes due to negative academic attitudes and study

behaviours. With regard to the proposed model of self-efficacy and vocational interest affecting

academic performance, limitations exist in that this model has not been empirically validated and

further research needs to be conducted in order provide quantitative data which verifies the

hypothesis that subject-specific interests directly affect performance in the related subjects.

The study of vocational interest and its influence on academic performance has not been well

researched both internationally and in South Africa, and it would be appropriate to investigate the

relationship between these constructs across a diverse range of schools or universities that offer

varying degrees of academic focus. In addition, the effect of vocational interest on subject-choice is

a related topic which warrants further research. School students may choose subjects for which they

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have no vocational interest and consequently this may affect their academic performance in those

subjects.

Considering the importance of Investigative and Realistic vocational interests in affecting academic

performance, it may be necessary to study different teaching environments and the way in which

schools and teaching staff either promote or inhibit the formulation of Investigative and Realistic

interests in school students. This research has important implications in that students with Realistic

interests may not perform well academically in an Investigative environment. The research may

produce results pertaining to person-environment fit which did not appear to be significant in this

study.

Finally, some of the factors measured by the ABAQ produced unexpected findings and it would

seem that further research needs to be conducted in order to verify the conclusions drawn in this

study. For example, the lack of significant relationship between coping strategies and academic

performance is contradictory to recent research reported by Zuckerman et al. (1998), Nonis et al.

(1998), Malefo (2000) and Collins and Onwuegbuzie (2003) who suggest that these constructs are

related. Furthermore, the unexpected finding that avoidance of procrastination is negatively related

to academic performance in this study also warrants further research as studies by Tice and

Baumeister (1997), Solomon and Rothblum (1984) and Rothblum et al. (1986) provide

contradictory findings.

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5.6 CONCLUSION

In conclusion, it can be stated that for the high school students in this sample, both cognitive and

non-cognitive factors influence academic performance. Specifically with regard to cognitive

factors, an increase in academic ability has a positive influence on academic performance and these

findings are consistent with a large volume of research. With regard to non-cognitive factors, the

results of this study suggest that vocational interests also influence academic performance. The

nature of this relationship is such that vocational interests which have much in common with

specific subjects will increase academic performance in that subject. However, the presence of

Investigative and Realistic interests will increase and decrease academic performance respectively,

no matter what subject is taken into account. Furthermore, certain academic attitudes and study

behaviours such as self-efficacy, achievement motivation, self-directedness in learning and the

avoidance of procrastination also appear to have had impact on the academic performance of the

high school students. It is important to note that these findings pertain to the participants in this

study. As indicated in preceding paragraphs, further research needs to be conducted to investigate to

what extent these findings can be generalised to the population of high school students in South

Africa.

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VOCATIONAL INTEREST AND OTHER NON-COGNITIVE FACTORS …